https://doi.org/10.31449/inf.v43i4.3008 Informatica 43 (2019) 573–579 573 
Decision Tree Algorithm Based University Graduate Employment Trend 
Prediction 
Fen Yang 
Henan University of Animal Husbandry and Economy, Zhengzhou, Henan 450044, China 
E-mail: yfh508@126.com 
Student paper 
Keywords: data mining, decision tree, employment prediction, C4.5 algorithm 
Received: November 21, 2018 
The employment situation of college graduates is becoming more and more serious. It is of great 
significance to find effective methods to predict the employment trend of students. In this study, C4.5 
algorithm was used to predict the employment trend of students. Taking the 2016 graduates of Henan 
University of Animal Husbandry and Economy as examples, four attributes affecting employment units 
were extracted, the information gain rate was calculated, the decision tree was constructed, and the 
classification rules were obtained. After data collection, conversion and cleaning, 420 employment 
records were obtained; 320 records were taken as the training samples. The classification rules were 
tested using 100 experimental samples, and the accuracy rate was 81%. Finally, the employment trend of 
the 2018 graduates was predicted by C4.5 algorithm, which provides a theoretical guidance for the 
arrangement of employment work in schools. Predicting the employment trend of students with decision 
tree algorithm is feasible and of great significance to the employment guidance of schools and the 
employment choice of students. 
Povzetek: V tem študentskem prispevku je bil uporabljen algoritem C4.5 za analizo zaposlitve študentov 
na Kitajskem. 
1 Introduction
The employment problem has gained more and more 
widespread social concern [1]. In recent years, the number 
of university graduates is increasing every year, and the 
employment situation is becoming more and more serious. 
It is very important for schools to analyze and study the 
information about students' employment, which can help 
them train students according to the market demand [2]. 
However, with the increase of the number of students, the 
data of employment information of graduates are 
accumulating continuously [3], which brings great 
difficulties to employment analysis. With the progress of 
science and technology, many new technologies have 
been applied in employment analysis. Wang [4] combined 
the residual modified GM (1,1) model with the improved 
neural network to predict the employment information 
index of graduate students, so as to predict the 
employment trend of graduate students.  He found that the 
mean square error decreased from 10-1 to 10-5 with the 
progress of training and the performance of the algorithm 
was the best when the gradient value and learning rate 
were 7.5912×10-5 and 0.8421. Liu et al. [5] proposed a 
method of information gain with weight based decision 
tree. The weighed based information gain was obtained by 
genetic algorithm. The decision tree was constructed and 
tested on undergraduates. They found that the method had 
a favourable prediction accuracy. Kwak et al. [6] found 
that education and gender were the most important factors 
affecting the employment of young and middle-aged 
people, while gender, health status and education were the 
most important factors affecting the elderly. Tan et al. [7] 
made the short-term employment forecast of Shandong 
province through independent component analysis (ICA) 
and found that the quality of labor force, industrial 
structure and income were the most important factors 
affecting employment. Decision tree algorithm shows a 
good performance in data prediction. Daga et al. [8] 
predicted high-risk renal transplantation using decision 
tree and random forest and found that the accuracy rate 
reached 85%, which provides accurate decision support 
for doctors. Mohreji et al. [9] combined with the decision 
tree algorithm to predict the delay of air transportation. 
Through the study of three New York airports, it was 
found that the confidence level of the prediction results 
was very high in at least 70% of the time. At present, most 
of the researches on employment trend focus on the 
influencing factors of employment, while few researches 
focus on the accurate prediction of employment trend, and 
the traditional data processing methods are difficult to 
extract useful information from the historical employment 
data. Therefore, in this study, C4.5 algorithm from 

574 Informatica 43 (2019) 573–579 F. Yang 
decision tree algorithm was used to extract four decision 
attributes from the employment-related information of the 
2016 graduates of Henan University of Animal Husbandry 
and Economy, and classification rules were extracted for 
employment trend prediction, so as to study the reliability 
of this method in employment prediction. 
2 Employment trend forecast and 
data mining 
Under the influence of population growth, popularization 
of higher education and expansion of enrollment, the 
number of college students has increased explosively, the 
number of graduates has risen sharply every year, and the 
employment situation has become more and more serious 
[10], which has aroused widespread concern of the 
society. The employment situation of college students is 
closely related to the future construction of schools, 
students' personal development and social stability [11]. 
The growth of graduates' employment rate can lead to 
economic growth [12]. In order to effectively manage the 
employment situation of students, colleges and 
universities have adopted information technology to 
collect and manage the employment information of 
students, in order to obtain valuable information, analyze 
the factors affecting employment, and help improve the 
employment situation. However, with the growth of the 
number of students, the data in the information 
management system is also growing rapidly. Traditional 
information analysis methods can not deal with such a 
large amount of information, nor can they fully play the 
potential value of these data. Although there are enough 
data, it is impossible to obtain the implicit association and 
rules between the data [13] and predict the future 
employment development based on these information. 
Therefore, an intelligent and reliable method is urgently 
needed to solve this problem. 
Data mining technology can process massive 
information quickly and efficiently and extract valuable 
information from it. It has a wide range of applications in 
fields such as business, industry and military. Mining and 
analyzing the employment information of graduates 
through data mining technology to obtain the factors 
affecting employment can help the school employment 
guidance center to guide the employment of students and 
promote employment. It can also predict the employment 
trend of graduates based on the information, so as to 
provide a decision-making basis for the adjustment of 
school teaching and employment work. 
3 Decision tree algorithm 
Decision tree algorithm is a typical technology of data 
mining. It can obtain valuable information by concluding 
and classifying data based on the attributes of data [14]. 
Applying decision tree algorithm in the analysis of 
employment information and obtaining relevant 
information affecting employment through construction of 
decision tree and extraction of classification rules is 
effective in predicting the future employment trend [15]. 
C4.5 algorithm from decision tree was used to process and 
analyze employment information. 
C4.5 algorithm is an improvement of ID3 algorithm 
[16], which selects the node attribute of tree based on 
information gain ratio. Data set 
K
 was defined, including 
k
 data samples, and its class attribute was set as 
m
 
values, corresponding to 
m
 categories 
) , , 2 , 1 ( m i C
i
 =
. If 
i
k
 refers to the number of samples 
in category 
i
C
, the amount of information needed by 
classification of a given object was called the entropy 
before division of 
K
, and its computational formula was: 

=
− =
m
i
i i m
p p k k k I
1
2 2 1
) ( log ) , , , (   (1) 
where 
i
p
(
k
k
p
i
i
=
) stands for the probability of a 
data object belonging to category 
i
C
. Entropy can reflect 
the average uncertainty and purity of data set. The larger 
the value of entropy, the higher the average uncertainty 
and the lower the purity.  
Suppose that there were 
w
 discrete attribute values in 
attribute 
A
 and set 
K
 was divided into 
w
 subsets, 
 
w
k k k , , ,
2 1

, and the samples in 
j
K
 had the same 
values in attribute 
A
, 
) , , 2 , 1 ( w j a
j
 =
. When 
A
 was 
taken as the testing attributes, these subsets were 
corresponding to some branch which grew from the node 
which contained set 
K
. Suppose 
ij
k
 as the number of 
sample in subset 
j
K
 belonging to category 
i
C
, then 
attribute 
A
 was  divided into the entropy of subset: 

=
+ + +
− =
w
j
mj j j
mj j j
s s s I
s
s s s
A E
1
2 1
2 1
) , , , ( ) ( 

 (2) 
The information gain of attribute 
A
 was: 
) ( ) , , , ( ) (
2 1
A E s s s I A Gain
m
− = 
 (3) 
The larger the information gain in the set, the higher 
the purity of subset division. 
The information gain ratio of attribute 
A
 was: 
) (
) (
) (
A SplitInfo
A Gain
A GainRatio =
, (4) 
where split information 
k
k k k
k
k k k
A SplitInfo
mj j j
w
j
mj j j
+ + + + + +
− =

=
 
2 1
1
2
2 1
log ) (
 
represents the span and uniformity of split data set 
K
 of 
attribute 
A
. 

Decision Tree Algorithm Based University Graduate Employment Trend Informatica 43 (2019) 573–579 575 
4 Decision tree algorithm based 
employment trend prediction 
4.1 Data collection and preprocessing 
The 2016 graduates of Henan University of Animal 
Husbandry and Economy were taken as research subjects. 
The basic information, achievement information and 
employment information of the students were obtained 
from the student status management system, student 
learning management system and student employment 
management system, and 500 records were selected as 
samples. 
There were many duplicate data or blank parts in the 
obtained data set, and the form of data was also not 
unified; hence preprocessing was needed. 
(1) Data integration:  
The data exported from the three systems were integrated 
into a table of general information, and the attributes are 
shown in Table 1. 
Name Major 
Gender Academic performance 
Politics status English competence  
Student cadre Computer skills 
Participation in student 
society 
Employment unit 
Table 1: General information. 
(2) Data correlation analysis:  
There were many irrelevant information in the data 
derived from the three systems, such as name, gender, 
politics status, student cadres, and participation in student 
society, which needed to be eliminated. 
(3) Data conversion:  
Noise was eliminated from the data. In order to facilitate 
statistics and analysis, it was necessary to generalize the 
remaining five attributes, i.e., divide major into three 
categories, popular, general and unpopular, divide 
academic performance into excellent, general and poor, 
divide the English competence into CET4 and above and 
below CET4, divide computer skills into level 3 and above 
and below level 3, and divide employment unit into state-
owned enterprise, private enterprise and others, 
represented by A, B and C. 
(4) Data cleaning:  
Duplicate data and blank data were deleted from the data, 
and finally 420 records were obtained, 320 of which were 
used as training samples and the remaining 100 was used 
for testing. 
4.2 Establishing decision tree 
The training samples were analyzed by taking 
employment unit (A, B and C) as the labeling attribute and 
major, academic performance, English competence and 
computer skills as decision attributes. The number of 
students under different categories of different attributes 
is shown in Table 2. 
Decision-making attribute A B C 
Major Popular 43 21 17 
General 12 38 66 
Unpopular 3 47 73 
Academic 
performance 
Excellent 21 17 50 
General 28 34 67 
Poor 18 26 59 
English 
competence 
CET4 and above 46 52 61 
Below CET4 36 46 79 
Computer 
skills 
Level 3 and 
above 
 
56 49 61 
Below level 3 62 58 34 
Table 2: Training data set. 
Suppose training sample as K and the corresponding 
subset and number of A, B and C as 
107 , 158 , 55
3 2 1
= = = k k k
 respectively. The entropy of 
K was calculated using equation (1). 
315878 . 2 ) 107 , 158 , 55 ( ) , , (
3 2 1
= = I k k k I
 
Then the information gains of different decision 
attributes were calculated using equation (2), (3) and (4). 
(1) Major 
Major was divided into popular, general and unpopular. 
When the major was popular, the entropy was: 
2145 . 1
81
27
log
81
27
81
32
log
81
32
81
43
log
81
43
) 27 , 32 , 43 (
2 2 2
= − − − = I
. 
When the major was general, then the entropy was: 
4511 . 1
116
66
log
116
66
116
38
log
116
38
116
12
log
116
12
) 66 , 38 , 12 (
2 2 2
= − − − = I
. 
When the major was unpopular, the entropy was: 
9512 . 0
123
73
log
123
73
123
47
log
123
47
123
3
log
123
3
) 73 , 47 , 3 (
2 2 2
= − − − = I
, 
then the entropy of attribute “major” was: 
5421 . 1 ) ( = major E
, 
the information gain was: 
0215 . 0 ) ( = major Gain
, 
information gain rate was: 
0131 . 0 ) ( = major GainRatio
. 
(2) Academic performance 
The academic performance was divided into excellent, 
general and poor. When the academic performance was 
excellent, the entropy was: 
2356 . 1
88
50
log
88
50
88
17
log
88
17
88
21
log
88
21
) 50 , 17 , 21 (
2 2 2
= − − − = I
. 
When the academic performance was general, the 
entropy was: 

576 Informatica 43 (2019) 573–579 F. Yang 
4201 . 1
129
67
log
129
67
129
34
log
129
34
129
28
log
129
28
) 67 , 34 , 28 (
2 2 2
= − − − = I
. 
When the academic performance was poor, the 
entropy was: 
2014 . 1
103
59
log
103
59
103
26
log
103
26
103
18
log
103
18
) 59 , 26 , 18 (
2 2 2
= − − − = I
, 
then the entropy of attribute academic performance 
was: 
4058 . 1 ) t achievemen specific - subject ( = E
,  
the information gain was 
0289 . 0 ) t achievemen specific - subject ( = Gain
, 
the information gain ratio was: 
0134 . 0 ) t achievemen specific - subject ( = GainRatio
. 
(3) English competence 
English competence was divided into CET4 and 
above and below CET4. When English competence was 
above CET4, the entropy was: 
8412 . 0
159
61
log
159
61
159
52
log
159
52
159
46
log
159
46
) 61 , 52 , 46 (
2 2 2
= − − − = I
. 
When English competence was below CET4, the 
entropy was: 
0258 . 1
161
79
log
161
79
161
46
log
161
46
161
36
log
161
36
) 79 , 46 , 36 (
2 2 2
= − − − = I
, 
then the entropy of attribute English competence was: 
3025 . 1 ) competence English ( = E
, 
information gain was: 
2157 . 0 ) competence English  ( = Gain
, 
information gain ratio was: 
1656 . 0 ) competence English ( = GainRatio
. 
(4) Computer skills 
Computer skills was divided into level 3 and above 
and below level 3. When computer skills was level 3 or 
above, the entropy was: 
9785 . 0
166
61
log
166
61
166
49
log
166
49
166
56
log
166
56
) 61 , 49 , 56 (
2 2 2
= − − − = I
,  
when computer skills was below level 3, the entropy 
was: 
6124 . 1
154
34
log
154
34
154
58
log
154
58
154
62
log
154
62
) 34 , 58 , 62 (
2 2 2
= − − − = I
,  
then the entropy of attribute competence skills was: 
4275 . 1 ) skills computer ( = E
, 
the information gain was: 
2144 . 0 ) competence ( = computer Gain
, 
the information gain rate was: 
1502 . 0 ) skills computer ( = GainRatio
. 
It was found from the above calculation results that 
the information gain rate of English competence was the 
largest. Therefore the attribute was regarded as the root 
node of decision tree. Then the information gain rate of 
every subtree was calculated according to the above 
procedures. Finally the decision tree in Figure 1 is 
obtained. 
4.3 Generating classification rules 
According to the decision tree in Figure 1, the following 
classification rules were obtained. 
(1) If English competence = CET 4 and above AND 
Computer skills = level 3 and above AND academic 
performance = excellent AND major = general Then 
employment unit = state-owned enterprise 
(2) If English competence = CET 4 and above AND 
Computer skills = below level 3 AND academic 
performance = excellent AND major = general Then 
employment unit = state-owned enterprise 
(3) If English competence = CET 4 and above AND 
Computer skills = below level 3 AND academic 
performance = general AND major = general Then 
employment unit = private enterprise 
(4) If English competence = CET 4 and above AND 
Computer skills = below level 3 AND academic 
performance = poor AND major = unpopular Then 
employment unit = private enterprise 
(5) If English competence = below CET4 AND computer 
skills = level 3 and above AND academic performance 
= excellent AND major = popular Then employment 
unit = state-owned enterprise 
(6) If English competence = below CET4 AND computer 
skills = level 3 and above AND academic performance 
= general AND major = general Then employment unit 
= private enterprise 
(7) If English competence = below CET4 AND computer 
skills = level 3 and above AND academic performance 
= excellent AND major = general Then employment 
unit = private enterprise 
(8) If English competence = below CET4 AND computer 
skills = below level 3 AND academic performance = 
poor AND major = unpopular Then employment unit 
= others 
(9) If English competence = below CET4 AND computer 
skills = below level 3 AND academic performance = 
poor AND major = unpopular Then employment unit 
= others 
It was concluded from the above classification rules 
that English competence and computer skills had the 
  
Figure 1: The decision tree of employment unit. 
Figure 2: The prediction of employment trend of the 
2018 graduates. 

Decision Tree Algorithm Based University Graduate Employment Trend Informatica 43 (2019) 573–579 577 
greatest impact on the employment units of students. 
Students with good English competence and excellent 
computer skills generally worked in state-owned or 
private enterprises, while students with poor English 
competence and weak computer skills, except for some 
students who had good academic performance or were 
major in popular subjects, did not work in the state-owned 
enterprises or private enterprises, which showed that 
schools need to strengthen the training of English and 
computer skills and pay more attention to these two 
aspects in the arrangement of teaching work and students 
themselves should strive to improve their English and 
computer skills and strengthen their competitive 
advantage in employment.  
4.4 Testing of classification performance 
The effectiveness of classification rules was tested 
through 100 experimental samples. Then the results were 
compared with the actual employment unit of students. 
The testing results are shown in Table 3. 
Sample Classification results Actual results 
1 A A 
2 B B 
3 A A 
4 C C 
5 B C 
6 B B 
7 B B 
……   
99 B B 
100 C C 
Table 3: The testing results of classification rules. 
The classification results of 81 samples were the same 
with the actual conditions, and the classification of 19 
samples was wrong; the accuracy rate was 81%. It 
indicated that the obtained classification rules were 
relatively accurate and could determine the employment 
condition of students. 
4.5 Prediction of employment trend 
After verifying the accuracy of the classification rules, the 
employment trend of graduates was predicted using the 
method proposed in this study. The 2018 graduates were 
taken as examples. The information about the major, 
academic performance, English competence and computer 
skills of the students were exported from the student status 
management system and the student learning management 
system. Then the employment trend of the graduates was 
predicted. The results are shown in Figure 2. 
Figure 2 demonstrates that the number of students 
who may be employed in private enterprises was the 
largest, accounting for 45%, while the number of students 
who may be employed in state-owned enterprises was the 
lowest, accounting for 21.56%. The decision tree and 
classification rules in this study could make a good 
prediction on the employment trend of graduates, help 
schools efficiently find the future employment direction of 
students, provide a strong basis for student employment 
guidance, and offer schools with valuable information. 
5 Discussion 
Employment has always been a problem that is difficult to 
be ignored and also can not be ignored in modern society, 
especially among university graduates. With the increase 
of the number of graduates, employment competition is 
becoming more and more fierce [17]. Employment is the 
most serious and difficult problem for graduates after they 
leave school and enter society, and it is also very important 
for schools. At present, all universities have employment 
guidance centers to collect and analyze the employment 
situation of students in order to find some rules and 
forecast employment. Employment prediction has great 
significance for graduates' employment and school 
teaching work [18]. However, with the increase of the 
number of university students and the accumulation of 
data, the analysis and processing of employment 
information is becoming more and more difficult, and it is 
difficult to obtain valuable information from mass data. 
The development of data mining technology has 
brought about new changes. Decision tree algorithm is an 
efficient classification method, and it is also applicable in 
the prediction of employment trend. In this study, C4.5 
algorithm which was relatively mature was selected. After 
obtaining the relevant data and information of graduates, 
four decision-making attributes, major, academic 
performance, English competence and computer skills, 
were extracted for analysis of employment units. The 
decision tree was established step by step after the 
calculation of information gain ratio of the attributes, and 
then classification rules were obtained through the 
decision tree. 
The information gain ratio of major, academic 
performance, English competence and computer skills 
was 0.0131, 0.0134, 0.1656 and 0.1502, respectively. It 
was found that English competence and computer skills 
were the most important factors affecting the employment 
of graduates. In the process of employment, English 
competence and computer skills are the signs of graduates' 
ability. Many employers have specific requirements for 
the English and computer skills of employees. At present, 
schools have attached great importance to the cultivation 
of students' abilities in these two aspects. The extensive 
arrangement of English courses and computer courses has 
promoted the improvement of students' abilities to a 
certain extent. Under the rigid requirements, they have to 
strengthen the study of these two aspects. However, 
passive learning is not enough. The importance of English 
and computer skills must be fully recognized, which can 
be fully illustrated by classification rules. The extraction 

578 Informatica 43 (2019) 573–579 F. Yang 
of classification rules can help schools and students 
clearly understand what ability is the most important and 
crucial. On the one hand, it is conducive to the 
arrangement of school teaching and employment guidance; 
on the other hand, it is also conducive to students' active 
learning. 
The testing of classification rules suggested that the 
classification rules obtained in this study had an accuracy 
rate of 81%, which showed that this method was feasible 
in predicting the employment trend of graduates. It was 
found from the employment trend prediction results of the 
2018 graduates that many students will be employed by 
private enterprises and few students will be employed by 
state-owned enterprises. It indicated that schools should 
strengthen the output of talents to state-owned enterprises 
and carry out targeted talent training. 
This paper preliminarily discussed the role of decision 
trees in college students' employment trend prediction, but 
there are still some problems that need further research: 
(1) more detailed division of employment units for 
college students is needed; 
(2) more factors that can affect college students' 
employment should be considered, such as family 
conditions, personal strengths, etc. For example, 
literature [19] points out that gender also can affect 
students' employment choices; 
(3) the possibility of the application of more data mining 
algorithms in the employment trend prediction of 
college students should be analyzed. For example, the 
Bayesian algorithm was used for employment 
prediction in literature [20]. 
6 Conclusion 
The decision tree algorithm can help handle and analyze 
the employment situation of students and understand the 
main factors affecting the employment of students. This 
study constructed the decision tree and extracted the 
classification rules through the four decision attributes, 
major, academic performance, English competence and 
computer skills. It was found that English competence and 
computer ability had the greatest impact on students' 
employment. The test suggested that the classification 
rules in this study had an accuracy of 81% and was 
feasible in predicting the employment trend of graduates. 
There are many shortcomings in this study. For examples, 
more decision attributes which can affect the employment 
units of students can be mined, employment units can be 
further divided to obtain more detailed employment trend, 
and a larger sample size is needed for determining the 
accuracy of the method. 
7 Acknowledgement 
This study was supported by the Research Project of 
Humanities and Social Sciences of Education Office of 
Hubei under grant number 16Z015. 
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