## 3 Requirement Analysis and System Overview  
### Data Description  
The questionnaire was designed by professionals from the graduate school of a university in Zhejiang Province, China, and its design was reasonable. It was distributed to all grades of graduate students (majors not counted) enrolled in the university at that time, and 677 questionnaires were collected in a timely and effective manner.  
In order to better discover the value of the data and facilitate the processing of the model, it is necessary to pre-process the data. Firstly, since some attributes cannot describe the intrinsic distribution pattern of the sample, such as the way and time of filling in the questionnaire, the independent variables were deleted to remove factors that had no effect on the results. Meanwhile, manual answer standardization was carried out for irregularities and implied information to turn them into data that can be processed normally. Secondly, for outliers and missing values in the questionnaire, we used the mean value of the corresponding attributes to fill in, while duplicate values were deleted or replaced with the mean value as appropriate. Finally, in order to better implement the data mining algorithm based on the questionnaire data, we classified the attributes into five modules, including basic information of student, academic participation of students, basic information of supervisors, academic guidance of supervisors, and academic achievements (evaluation indicator). Each module contains the corresponding attributes. By vectorizing the options of each attribute with the numerical replacement of A-1, B-2, C-3, D-4, and E-5, the questionnaire data were converted into feature vectors required by the data mining algorithms. More detailed data will be shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{l l l c c c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**Value**} & \multicolumn{2}{c}{**All**} & \multicolumn{2}{c}{**Third Year**} & \multicolumn{2}{c}{**Second Year**} & \multicolumn{2}{c}{**First Year**} \\ \cline{3-10}  & & & **No.** & **PCT** & **No.** & **PCT** & **No.** & **PCT** & **No.** & **PCT** \\ \hline \multirow{4}{*}{\
\begin{tabular}{l} Students’ \\ \end{tabular} } & Gender & Male & 225 & 33.23\% & 45 & 29.41\% & 89 & 33.09\% & 91 & 35.69\% \\ \cline{2-10}  & Female & 452 & 66.77\% & 108 & 70.59\% & 180 & 66.91\% & 164 & 64.31\% \\ \cline{2-10}  & Recent graduates & Yes & 495 & 73.12\% & 123 & 80.39\% & 191 & 71.00\% & 181 & 70.98\% \\ \cline{2-10}  & No & 182 & 26.88\% & 30 & 19.61\% & 78 & 29.00\% & 74 & 29.02\% \\ \cline{2-10}  & Bachelor’s degree from & Yes & 155 & 22.90\% & 27 & 17.65\% & 62 & 23.05\% & 66 & 25.88\% \\ \cline{2-10}  & our university (Sbasic5) & NO & 522 & 77.10\% & 126 & 82.35\% & 207 & 76.95\% & 189 & 74.12\% \\ \cline{2-10}  & & PH D & 78 & 11.52\% & 8 & 5.23\% & 28 & 10.41\% & 42 & 16.47\% \\ \cline{2-10}  & Plan after graduation & Civil Servant & 192 & 28.36\% & 43 & 28.10\% & 80 & 29.74\% & 69 & 27.06\% \\ \cline{2-10}  & (Sbasic6) & Staff & 366 & 54.06\% & 96 & 62.75\% & 143 & 53.16\% & 127 & 49.80\% \\ \cline{2-10}  & Others & 41 & 6.06\% & 6 & 3.92\% & 18 & 6.69\% & 17 & 6.67\% \\ \hline \multirow{4}{*}{
\begin{tabular}{l} Participation of academic \\ competition (Sbasic7) \\ \end{tabular} } & None & 409 & 53.59\% & 82 & 53.90\% & 145 & 71.37\% & 182 & 60.41\% \\ \cline{2-10}  & School & 185 & 29.41\% & 45 & 31.23\% & 84 & 21.96\% & 56 & 27.33\% \\ \cline{2-10}  & Province & 42 & 13.07\% & 20 & 4.83\% & 13 & 3.53\% & 9 & 6.20\% \\ \cline{2-10}  & National & 41 & 3.92\% & 6 & 10.04\% & 27 & 3.14\% & 8 & 6.06\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The descriptive results of questionnaires.