The invention discloses a Chinese text abstract generation method based on a sequence-to-sequence model, and the method comprises the steps: firstly carrying out the word segmentation of a text, filling the text to a fixed length, and carrying out the Gaussian random initialization of a word vector; encoding the text, inputting the encoded text into a bidirectional long short-term memory (LSTM) network, and taking the final output state as precoding; performing convolutional neural network (CNN) on the word vectors according to different window sizes, and outputting the word vectors as windowword vectors; constructing an encoder, constructing a bidirectional LSTM (Long Short Term Memory), taking precoding as an initialization parameter of the bidirectional LSTM, and taking a window word vector in the previous step as input; and constructing a decoder, and generating a text by using a one-way LSTM and combining an attention mechanism. According to the method, a traditional encoder froma sequence to a sequence model is improved, so that the model can obtain more original text information in an encoding stage, a better text abstract is finally decoded, and a word vector with smallerfine granularity is used, so that the method is more suitable for a Chinese text.