Chinese calligraphy is one of the representatives of Chinese traditional culture. However, the different styles, complex structures, and various distortions of calligraphic fonts have brought great obstacles to learning and appreciating calligraphy for the public. A calligraphic font recognition algorithm based on an improved DenseNet network is proposed to solve the difficulty of ordinary people in interpreting calligraphy works. A regional weight ratio pooling rule is designed to replace the maximum pooling and average pooling rules of the traditional DenseNet network. The Nadam algorithm is used to adjust the adaptive learning rate and optimize the model training effect. In addition, a model pruning strategy based on the pruning technology is proposed, which ensures a strong recognition performance and improves the training efficiency of the model. The experimental results show that in a mixed font data set composed of four types of fonts, namely the standard script, the running script, the clerical script, and the seal script, the proposed algorithm obtains a recognition rate of 96.13%, which is better than those of the other five deep learning models.