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Research on Personalized Recommendation Strategy for Teaching Content of Sports Culture Based on Deep Learning

  
21 mars 2025
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Figure 1.

Platform overall architecture diagram
Platform overall architecture diagram

Figure 2.

Check the accuracy, recall rate and f1 value
Check the accuracy, recall rate and f1 value

Figure 3.

Recommended results
Recommended results

The system improves the physical level of students

Preexperiment Postexperiment
Height (cm) 178.21±5.82 176.85±6.45
Weight (kg) 94.26±14.85 93.45±14.6
BMI 30.47±4.02 30.92±4.15**
Lung activity (ml) 4107.12±568.22 4290.84±636.94*
50 meters (s) 8.22±1.25 7.72±0.60*
1000 meters (s) 349.84±53.94 311.94±46.78**
Predisposition (cm) 3.12±4.42 4.53±5.72
The lead is up (frequency) 0.5±0.94 1.35±1.83
Fixed jump (cm) 187.46±25.13 208.63±25.66*

Physical resource security system resources

Resource type Database name Number of resources Metadata field number
Figures The sports industry expert 940 20
Paper Journal paper 32000 110
Paper Dr. Pegatron’s dissertation 46000 106
Book Chinese book 25000 20
Book Foreign library 2348 12
Periodicals Foreign journal 597 18
Book Library of the republic 680 14
Periodicals journal 422 25
Paper Undergraduate dissertation 16500 23
Newspapers The sports newspaper 270000 30
Paper Sports meeting paper 3300 115
Video Video 100 15
News Athlete report 8320 14
News Sports team report 5934 11
News Trainer report 1552 10
News Sports related articles 16000 18
Collection information Other institutions’ collection information 610000 30

Sports knowledge, awareness and behavioral improvement

Preexperiment Postexperiment
Sports awareness (division) 88.21±13.52 95.25±10.74**
Sports knowledge (division) 80.73±14.53 87.26±11.14**
Physical activity (minute) 26.88±7.38 37.85±5.68**