Abstract: In recent years, with the rapid development of artificial intelligence (AI) technology, researches on automatic solving of Math Word Problems (MWP) have been improved. In the task of automatically solving MWP, the modeling of the problem text is very important. For this issue, a Dual Channel Text Encoder (DCTE) based on Recursive Neural Network (RNN) and Transformer is proposed. DCTE firstly uses an RNN to initially encode the problem text, and then uses the Transformer based on the self-attention mechanism to obtain the long-range contextual information to enhance the representation of the word and problem text. Combining DCTE and GTS (Goal-Driven Tree-structured MWP Solver) decoder to obtain our math word problem solver DCTE-GTS. In this paper, DCTE-GTS is experimented on Math23k dataset and compared with Graph2Tree, HMS and other models. Meanwhile, the ablation experiments are also conducted to explore impact of encoder configuration on model's perfor-mance. The experimental results show that the proposed DCTE-GTS is better than the baseline models, obtaining an answer accuracy of 77.6%. The ablation experiments show that the configuration of DCTE is the best.