Abstract: Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity ranking. It is of great significance to some NLP (natural language processing) tasks, such as question answering. Unlike English entity linking, Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words, which is more evident in certain scenarios. In Chinese domains, such as industry, the generated candidate entities are usually composed of long strings and are heavily nested. In addition, the meanings of the words that make up industrial entities are sometimes ambiguous. Their semantic space is a subspace of the general word embedding space, and thus each entity word needs to get its exact meanings. Therefore, we propose two schemes to achieve better Chinese entity linking. First, we implement an n-gram based candidate entity generation method to increase the recall rate and reduce the nesting noise. Then, we enhance the corresponding candidate entity ranking mechanism by introducing sense embedding. Considering the contradiction between the ambiguity of word vectors and the single sense of the industrial domain, we design a sense embedding model based on graph clustering, which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with context. We test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general scenarios. We confirm that our method can better learn candidate entities’ fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.