本文使用具有兩層隱藏層的多層感知器做為深度學習的模型。標籤集有8個類別，共有230個檔案，總計1745筆，而測試集有36個檔案，總計276筆。資料預處理使每筆的特徵欄位數量為50、標籤欄位數量為8，標籤欄位採用一位有效編碼。標籤集的80%作為訓練、20%作為驗證，評估模型的指標是準確率，分類結果顯示：訓練集與驗證集的準確率分別為100%與99.7%。

This paper uses Multilayer Perceptron with two hidden layers as a deep learning model. The labeled dataset has 8 categories, a total of 230 files, a total of 1745 records. The test dataset has 36 files, a total of 276 records. Data preprocessing makes the number of feature columns for each record 50 and the number of label columns 8. The label columns use One-hot encoding. 80% of the labeled dataset is used for training and 20% is used for validation. The metric of evaluating the model is accuracy. The classification results show that the accuracy of training dataset and validation dataset are 100% and 99.7% respectively.