Aiming at the problem that the existing Handwritten Mathematical Expression Recognition （HMER） methods reduce image resolution and lose feature information after multiple pooling operations in Convolutional Neural Network （CNN）， which leads to parsing errors， an encoder-decoder model for HMER based on attention mechanism was proposed. Firstly， Densely connected convolutional Network （DenseNet） was used as the encoder， so that the dense connections were used to enhance feature extraction， promote gradient propagation， and alleviate vanishing gradient. Secondly， Gated Recurrent Unit （GRU） was used as the decoder， and attention mechanism was introduced， so that， the attention was allocated to different regions of image to realize symbol recognition and structural analysis accurately. Finally， the handwritten mathematical expression images were encoded， and the encoding results were decoded into LaTeX sequences. Experimental results on Competition on Recognition of Online Handwritten Mathematical Expressions （CROHME） dataset show that the proposed model has the recognition rate improved to 40.39%. And within the allowable error range of three levels， the model has the recognition rate improved to 52.74%， 58.82% and 62.98%， respectively. Compared with the Bidirectional Long Short-Term Memory （BLSTM） network model， the proposed model increases the recognition rate by 3.17 percentage points. And within the allowable error range of three levels， the proposed model has the recognition rate increased by 8.52 percentage points， 11.56 percentage points， and 12.78 percentage points， respectively. It can be seen that the proposed model can accurately parse the handwritten mathematical expression images， generate LaTeX sequences， and improve the recognition rate.