text-classification-cnn-rnn and text-cnn
These are competitors offering alternative implementations of the same core approach—both apply convolutional neural networks to Chinese text classification, with the primary difference being that B explicitly incorporates Word2vec embeddings while A combines CNN with RNN architecture for potentially better sequential context capture.
About text-classification-cnn-rnn
gaussic/text-classification-cnn-rnn
CNN-RNN中文文本分类，基于TensorFlow
Implements character-level CNN and RNN architectures using TensorFlow 1.3+ with Conv1D operations and multi-layer GRU/LSTM cells for sequence modeling. Provides complete preprocessing pipeline including vocabulary building, fixed-length sequence padding (600 characters), and batch iteration with shuffling for the THUCNews dataset (10 categories, 65K training samples). Achieves 96%+ test accuracy on Chinese news classification with detailed evaluation metrics including per-category precision/recall and confusion matrices.
About text-cnn
cjymz886/text-cnn
嵌入Word2vec词向量的CNN中文文本分类
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