A Method of Mining Abnormal Data of College Students’ Physical Fitness Test Based on Deep Learning

In order to provide effective reference data for the improvement of college students’ physique, the depth learning algorithm is used to optimize the design of abnormal data mining method for college students’ physique test. Use the hardware equipment to obtain the college students’ physique test data samples, according to the designed student physique test anomaly detection criteria, use the deep learning algorithm to extract the physical test data features, and determine whether the current data is the mining target. After the mining target is obtained from the data sample, the association rules of abnormal data mining are generated, and the final abnormal data mining results of college students’ physique test are obtained through the steps of missing data interpolation and repeated data filtering. Through the comparison with traditional mining methods, the conclusion is drawn that the accuracy and recall of the optimized design of outlier data mining methods have been significantly improved.