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Handwritten Chinese character recognition is the basis of handwritten Chinese characters input system. At present, Handwritten Chinese character input method in smart devices can not adjust the recognition model dynamically according to the user"s Chinese writing habits in order to improving the correct recognition rate of handwritten Chinese characters. Through the study of the latest deep learning algorithm and training model, a design method of handwritten Chinese character input system based on the real-time acquisition of handwritten Chinese characters samples is proposed. This method will capture the user"s handwriting Chinese characters as the increment of sample. Through re-training the pre-training model, the recognition model can better fit the user"s writing habits, and enhance the recognition rate of the Chinese characters handwritten input system. Finally, on the basis of the theoretical method and combining the new design of depth residual network, a comparative experiment of handwritten Chinese character recognition is carried out. The experimental results show that by introducing real-time samples to training, the rate of recognition of handwritten Chinese characters recognition model is improved. It can more effectively satisfy the user"s demand for handwriting Chinese character input system at the intelligent terminal.