Simulation of University Teaching Achievement Evaluation Based on Deep Learning and Improved Vector Machine Algorithm

Figures & data

Figure 1. Tower structure of big data analysis and evaluation index system.

Figure 2. Flow chart of ideology TQA model.

Figure 3. Schematic diagram of SVM algorithm.

Figure 4. Schematic diagram of DBT-SVM multi-class algorithm.

Table 1. The number of data samples in each group of the adult dataset.

DataSet	Samples	DataSet
A1a	1605	A1a
A2a	2655	A2a
A3a	3182	A3a
A4a	4651	A4a
A5a	6254	A5a
A6a	11250	A6a

Figure 5. Multivariate linear regression.

Figure 6. Neural network model.

Figure 7. SVM model.

Table 2. Prediction results of each model.

Sample	Multivariate linearity		Partial least squares		Neural network		SVM
	Predicted value	Relative error	Predicted value	Relative error	Predicted value	Relative error	Predicted value	Relative error
26	7.5155	7.1551	7.6513	9.45421	6.4543	0.4646	6.4527	1.646
27	6.4213	5.6462	6.4211	3.4515	6.4223	5.4521	7.5133	2.76
28	6.4155	6.4523	7.2513	2.4646	7.1555	4.5166	7.5913	1.16
29	8.5422	4.5023	8.4455	6.4455	9.4542	4.0213	9.4123	3.25
30	7.4815	5.4533	7.6516	5.4551	7.1523	5.4863	8.1537	0.73
MSE	0.16163		0.19372		0.46637		0.00466
CPU time/s	0.04646		0.44636		2.7961		0.24963
Sample	Multivariate linearity		Partial least squares		Neural network		SVM

Data Availability Statement

The labeled dataset used to support the findings of this study are available from the corresponding author upon request.