Word-level completion can automatically complete words as the translator types character sequences. Word-level completion can accelerate the editing process of human translation and ensure the translation quality. Although significant progress has been made in the field, there may be multiple candidate words when models predict words. Multiple words make up a list of candidate words. We improve the existing model by determining the most credible word in the candidate word list. We propose a multi-model fusion method to increase the accuracy of word-level completion. The improved model can use multiple evaluation criteria (Lesk method, WordNet knowledge base, and pre-training model) to calculate the scores of words by classification and weighting. The word with the highest score is selected as the most credible word. The experimental results prove that our proposed method is effective. In De \(\longrightarrow \) En, our method improves the accuracy by 2.83%. In Zh \(\longrightarrow \) En, our method improves the accuracy by 2.77%.