The Innovation of Ideological and Political Education Integrating Artificial Intelligence Big Data with the Support of Wireless Network

Figures & data

Figure 1. Structure of CNN.

Figure 2. CNN connection diagram.

Figure 3. CNN multi-channel feature extraction model.

Figure 4. Main behaviors of the human body.

Figure 5. Flow chart of ordinary human body recognition.

Figure 6. Common mobile phone sensors.

Figure 7. Gravity sensor of mobile phone.

Figure 8. Computer equipment used in the experiment.

Figure 9. Result chart of recognition accuracy.

Table 1. Experimental result.

	Research model	CNN-SVM	CNN-BP
Walking	98.59191867	98.31768	98.50482
Upstairs	98.99048112	98.74312	98.69261
Go downstairs	99.21485463	98.60293	99.00732
Lying	100	100	100
Sit	97.10095189	93.27041	95.2642
Standing	95.60577608	93.59483	94.39811
Run	100	100	100

Figure 10. Reliability and validity analysis results.

Table 2. Comparison result.

	The proposed model	CNN-SVM	CNN-BP
Accuracy	0.9783	0.9683	0.9731
Precision	0.9782	0.9684	0.9732
Recall	0.9783	0.9683	0.9731

Figure 11. Percentage of algorithm prediction accuracy.

Figure 12. Result of the SSE.Note: Series 1 is the result of the SSE, and series 2 is the minimum SSE

Figure 13. Model fitness function curve.