Attention based neural network translation is the mainstream technology in the field of machine translation.At the moment,a variety of attention gene rating mechanisms has been proposed.Although these attention generation mechani sms have their own advantages and disadvantages,each kind of attention generati on mechanism cannot make full use of all the known information.Every attention generation mechanism has a certain gap with the theoretical real attention.In t his paper,a variety of attention generation mechanisms are used to generate the ir own attention,and then these attentions are weighted and normalized to guide the decoder for further translation process.Similar to the democratic decision -making process which achieves better decision-making accuracy than the arbitr ar y ones,this mechanism can obtain relatively more accurate attention value,thus improving the overall quality of translation.Based on the above algorithms,th is paper designs a new algorithm MA-CTT using CNN,Transformer and Tree Transfor m er to generate a combined attention on the open German English corpus(IWSLT14). This paper combines the advantages of CNN,Transformer and Tree Transformer.Acc ording to the experimental data,the BLEU value of the algorithm reaches 32.61o n the open German English corpus (IWSLT14).Compared with the baseline algorithms ,the translation accuracy of this algorithm has been improved.