The over-translation and under-translation problem in neural machine translation could be alleviated by coverage model. The existing methods usually store the coverage information in a single way, such as coverage vector or coverage score, but do not take the relationship among different coverage methods into consideration, leading to insufficient use of the information. This study proposes a multi-coverage mechanism based on the consistency of translation history and complementarity between different models. The concept of word-level coverage score is defined first, and then the coverage information stored in both coverage vector and coverage score are incorporated into the attention mechanism simultaneously, aiming to reduce the influence of information loss. According to different fusion methods, two models are introduced. Experiments are carried out on Chinese-to-English translation task based on sequence-to-sequence model. Results show that the proposed method could significantly enhance translation performance and improve alignment quality between source and target words. Compared with the model with coverage vector only, the number of over-translation and under-translation problem is further reduced.