Since the influence of the psychological and physiological factors of the writer on the writing activity was neglected while collecting the offline Chinese handwritten samples, the generalization ability of the traditional handwriting identification algorithm was low. A Chinese character handwriting identification algorithm based on capsule network was proposed, and a complex background of tracking the collected datasets to simulate Chinese characters was constructed. The capsule network constructed an activity vector to represent a specific type of instantiation parameter. The dynamic routing algorithm routed the activity vector to the corresponding capsule in the next layer to enable the next layer capsule to get a clearer input signal. The experimental results of five algorithms in HWDB dataset and tracking acquisition dataset showed that the classification accuracy of this algorithm was higher than that of the other four algorithms. The classification accuracy of HWDB dataset and tracking dataset algorithm were respectively 95.82% and 94.39%. The algorithm had strong generalization performance and low dependence on the number of training samples, making up for the convolutional neural network pooling layer information lost.