Research on the Application of Machine Learning for Marxist Education Integrating Chinese Excellent Traditional Culture

Figure 1.

Figure 2.

Each evaluation index score and final score

Index	2016	2017	2018	2019	2020	2021	2022	2023
A	2.265	4.796	3.964	1.182	4.728	1.167	2.588	1.206
B	4.282	2.276	2.659	1.86	4.196	2.065	4.131	2.453
C	1.819	2.685	2.373	3.825	3.656	1.574	2.348	2.047
D	3.215	1.679	1.028	4.162	2.537	0.868	4.260	1.488
E	1.432	0.949	1.495	4.532	2.615	3.632	4.198	3.105
Total	2.544	2.394	2.216	3.158	3.746	1.839	3.854	2.053

The MAE and RMSE results of resource recommendation results

Group	Our model		RRS 1		RRS 2
	MAE	RMSE	MAE	RMSE	MAE	RMSE
1	0.558	0.803	0.789	0.870	0.929	0.956
2	0.561	0.779	0.848	0.906	0.845	1.008
3	0.627	0.819	0.799	0.837	0.867	1.015
4	0.616	0.808	0.761	0.879	0.901	1.021
5	0.665	0.823	0.788	0.909	0.868	1.018
6	0.586	0.791	0.757	0.894	0.824	0.911
7	0.677	0.751	0.795	0.975	0.918	1.043
8	0.634	0.787	0.785	0.864	0.823	0.977
9	0.652	0.758	0.818	0.916	0.906	1.001
10	0.624	0.769	0.792	0.942	0.882	0.992

The comparison of the results of the evaluation results of Marxist teaching effect

Methods	Training set		Test set
	RMSE	R	RMSE	R
ICS-ELM	0.007	0.964	0.036	0.984
PSO-ELM	0.018	0.955	0.040	0.965
GA-ELM	0.015	0.947	0.045	0.937
ELM	0.019	0.932	0.055	0.926

Comparison of the effect of teaching resource recommendation model

Item	Before applying	After applying	t value	P value
Degree of understanding	2.22±0.35	2.69±0.36	-9.554	0.003
Cognition of teaching resources	2.56±0.52	2.62±0.53	-3.615	0.001
Model characteristics	3.14±0.38	3.57±0.36	3.566	0.008
Attention concentration	3.24±0.52	3.65±0.53	1.628	0.058
Study interest	2.56±0.61	2.77±0.63	2.591	0.004