A model framework called DenseNet-SE is proposed based on a multi-featured dense convolutional neural network. Compared with the conventional methods, the DenseNet-SE adopts the data-driven approach and the manual extraction of features is not necessary. It contains the dense residual blocks so that the deep features can be acquired. In the jump-joining way, the fine-grained features are obtained from the shallow layers to assist the deep features. The fused features can help the network structure obtain more global information and better represent the categories of formula symbols. The standard mathematical formula symbol library provided by the competition organization on recognition of online handwritten mathematical expression (CROHME) is used to verify the proposed algorithm, results show that the recognition rates of CROHME2014 and CROHME2016 are 93.38% and 92.93%, respectively, higher than those of the existing algorithms.