This project implements a convolutional neural network for Chinese text classification, using pre-trained Word2vec embeddings as input features. The model classifies text into 10 categories (sports, finance, real estate, etc.) using the THUCNews dataset. It achieves 97.2% test accuracy, improving upon the baseline by incorporating word-level embeddings, multi-size convolutional kernels, and regularization techniques.