Word sense disambiguation (WSD) is a challenging problem in natural language processing. As an excellent semi-supervised disambiguation algorithm in WSD, the genetic max-minant system word sense disambiguation (GMMSWSD) can perform full-text WSD quickly. The algorithm uses a graph based on local context to represent semantic relationships for word sense disambiguation. However, in the process of disambiguation, global semantic information is lost and inconsistent  disambiguation results occur, which leads to lower accuracy of the algorithm. We therefore propose an improved graph model based on global domain and short-term memory factor to solve the abovementioned problems. The new graph model introduces global domain information to enhance the processing ability of global semantic information. At the same time, according to the principle of short-term memory, we introduce the short-term memory factor into the model, which can enhance the linear relationship between semantics and avoid the influence of inconsistent disambiguation results on word sense disambiguation. Experimental results show that compared with the classical word sense disambiguation algorithm, the proposal's precision of word sense disambiguation is improved.