# Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network
## 4 Results and Discussion
### Analysis of Experimental Results
#### 4.3.2 Comparison Experiment  
The algorithm used in this paper is compared with the traditional machine learning classification algorithms: Bayesian network (BN), KNN, support vector machine (SVM), and multilayer perception machine (MLP). The experimental comparison results are shown in Table 3.  
According to the experimental results in Table 3, the classification effect of the naive Bayesian algorithm and the K nearest neighbor algorithm is po  
Figure 5: (**a**) Comparison curve of accuracy between CNN and AE-CNN; (**b**) comparison curve of loss between CNN and AE-CNN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **ACC** & **Recall** & **Specificity** & **F1-Score** & **Precision** \\ \hline BN & 0.7837 \(\pm\) 1.61 & 0.7344 \(\pm\) 1.64 & 0.7956 \(\pm\) 1.48 & 0.7660 \(\pm\) 1.83 & 0.6973 \(\pm\) 1.75 \\ KNN & 0.7581 \(\pm\) 2.34 & 0.8166 \(\pm\) 1.05 & 0.7836 \(\pm\) 1.74 & 0.8420 \(\pm\) 1.06 & 0.7562 \(\pm\) 0.85 \\ SVM & 0.8327 \(\pm\) 0.81 & 0.7490 \(\pm\) 0.85 & 0.8864 \(\pm\) 0.92 & 0.8310 \(\pm\) 0.94 & 0.7817 \(\pm\) 0.86 \\ MLP & 0.8763 \(\pm\) 1.05 & 0.8650 \(\pm\) 1.38 & 0.8800 \(\pm\) 1.01 & 0.8420 \(\pm\) 1.05 & 0.8084 \(\pm\) 1.06 \\ AE-CNN & 0.9334 \(\pm\) 0.21 & 0.9325 \(\pm\) 0.26 & 0.9370 \(\pm\) 0.30 & 0.9297 \(\pm\) 0.19 & 0.9366 \(\pm\) 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of prediction effects of different classification algorithms.  
the algorithm principle of Naive Bayes is to give the output category through a priori and a posteriori probabilities. Once there is a high correlation between sample data, the classification effect will be poor. The core idea of the K nearest neighbor algorithm is to use the principle of proximity in the feature space for classification, but most of the risk feature data are discrete, lacking correlation between features, so the prediction accuracy is insufficient. Support vector machines and multi-layer perceptrons perform well. A support vector machine maps low-dimensional linear indivisible samples to high-dimensional separable space through various kernel functions. However, with the increasing amount of data, the mapping dimension of kernel functions in support vector machines will continue to increase, which affects the timeliness of the prediction model. Therefore, this model is not suitable for processing large-scale sample data. With the improvement in computing power of computer hardware, the multilayer perceptron performs well in algorithm performance, but a single multilayer perceptron cannot compress discrete high-dimensional features in advance when dealing with a high-dimensional sparse matrix, thus increasing the computational load of model training, so the prediction effect will be affected. The experimental results show that the AE-CNN algorithm used in this paper is superior to several traditional classification algorithms, which shows that the algorithm model in this paper is effective, can better deal with the sports risk prediction problem, and has a stronger ability to extract discrete data features. The extracted features are more robust, and the model is more generalized and accurate.