TEXT CLASSIFICATION MODEL CONSTRUCTION METHOD AND APPARATUS, AND TERMINAL AND STORAGE MEDIUM

摘要

A text classification model construction method and apparatus, and a terminal and a storage medium. The text classification model construction method comprises: constructing a convolutional neural network model by using a PyTorch framework, wherein the convolutional neural network model is arranged on an embedding layer (S11); obtaining text classification training data, and performing word vector training on the text training data by using a Word2Vec algorithm to obtain word vectors (S12); and inputting the word vectors into the convolutional neural network model for classification training, and obtaining a text classification model when convergence occurs (S13). A PyTorch framework is used for the text classification model. Due to the fact that the object-oriented interface design of the PyTorch framework comes from the Torch, the interface design of the Torch has the advantages of being flexible and easy to use, the PyTorch framework can print calculation results layer by layer to facilitate debugging, and therefore, the constructed text classification model is easier to maintain and debug.