Research on an Interactive Question Answering System of Artificial Intelligence Customer Service Based on Word2Vec

In order to reduce the labor cost pressure of telecom operators' customer service and improve the service quality, the natural language analysis technology based on artificial intelligence technology will realize the automatic question and answer customer service. This paper proposes to obtain word vectors based on Word2vec model. By comparing the word vectors under different training model parameters, the results show that the low-frequency word threshold plays a better role in controlling the number of the final trained word vectors. The training results of SKIP-GRAM model are better than that of CBOW, and the word list is more regular. Under the condition of making full use of the existing customer service knowledge resources, the new system will realize the goal of innovating service means, expanding customer service channels, diverting customer service pressure, and improving service efficiency.