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Based on the convolution system guided by the target information, this paper summarizes the relevant source information and proposes a systematic processing language method. By using different guide signals in the decoding process, the specially designed convolution + gating architecture can identify the source sentence part related to the predicted target word, and fuse it with the context of the whole source sentence to form a unified representation. The results show that the model feeds the representation and the target language words into DNN to form a stronger neural network joint model (NNJM). The experimental results of two NIST Chinese-English translation tasks show that under the same settings, the improvement of tagCNN and inCNN on the Dep2str baseline is + 1.28 and + 1.75 BLEU, respectively. The proposed model is superior to the average of NIST MT04 and MT05 + 0.36 and + 0.83 BLEU, respectively, which is + 1.08 BLEU higher than the traditional DNN machine translation. The model provides a new way for statistical machine translation research.