# Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network
## 3 Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network
### Construction of Sports Risk Prediction Model
#### 3.3.3 AE-CNN Sports Risk Prediction Algorithm Flow  
To solve the problems of difficult extraction of motion risk features and insufficient prediction accuracy, this chapter combines AE and CNN to build a motion risk prediction model, aiming to take advantage of self-encoders and convolutional neural networks to improve the performance and efficiency of deep learning models in motion risk prediction. AE-CNN contains a self-encoder module for feature extraction and dimensionality reduction, and the self-encoder can completely express motion. The AE-CNN includes a self-encoder module for feature extraction and dimensionality reduction, which is able to fully represent the features of the risk and can improve the efficient use of raw data. The self-encoder module consists of an encoder and a decoder, where the encoder converts the input data into a low-dimensional dense representation and the decoder converts the low-dimensional representation back to the original data. The learning process of the self-encoder is unsupervised, which can make full use of the information in the unlabeled data. Afterwards, the output of the self-encoder is fed into a convolutional neural network for classification prediction. The convolutional neural network consists of a convolutional layer, a pooling layer, and a fully connected layer, which can automatically learn a high-dimensional nonlinear feature representation and accomplish causal inference between risk features and risk factors. In this process, the feature extractor of the self-encoder module can reduce the noise and redundant information of the input data and improve the performance and efficiency of the convolutional neural network. Finally,AE-CNN achieves end-to-end learning by using feature transfer between the two modules. The self-encoder part extracts the features of the input data, and the convolutional neural network uses these features to further improve performance. The whole AE-CNN model can be trained through supervised or unsupervised learning. This model can effectively extract feature information and better solve the problem that traditional models are difficult to handle for high-dimensional and nonlinear data. The motion risk prediction model based on auto-coding and convolutional neural networks proposed in this chapter consists of four modules: the data set equalization module, feature selection module, self-encoder feature extraction module, and convolutional neural network prediction module. Specifically, firstly, the BSL sampling technique is applied to perform the data set equalization operation and update the data set. Secondly, the feature selection of the equalized dataset is performed by the IG method to determine the sample variables. Then AE is used to perform feature dimensionality reduction and hidden representation extraction, which are used as the set of sensitive features that can reflect the risk category as the input of CNN. Finally, the delineated training set is used to learn the structure of the CNN, and the directed relationship between risk features and risk types is obtained to perform the prediction of motion risk categories and derive the corresponding risk categories. In the AE-CNN model, the convolutional neural network can also avoid overfitting by using operations such as pooling and dropout. To improve the prediction accuracy of the AE-CNN model, feature selection and fusion techniques are applied to the model. Specifically, features that have a better correlation to the target variables are selected from a large amount of personal health data using feature selection techniques. Through feature fusion techniques, data features from different modalities are combined to improve the accuracy and robustness of feature characterization.