Almost all existing knowledge-based word sense disambiguation (WSD) methods used exploit context information contain, in certain window size around ambiguous word, are ineffective because all words in the window size have the same impact on determining the sense of ambiguous word. In order to solve the problem, this paper proposes a novel WSD model based on distance between words, which is built on the basics of traditional graph WSD model and can make full use of distance information. Through model reconstruction, optimization, parameter estimation and evaluation of comparison, the study demonstrates the feature of the new model: The words nearby ambiguous word will have more impact to the final sense of ambiguous word while the words far away from it will have less. Experimental results show that the proposed model can improve Chinese WSD performance, compared with the best evaluation results of SemEval-2007: task #5, this model gets MacroAve (macro-average accuracy) increase 3.1%.