Intelligent customer service accurately answers users’ inquiries with artificial intelligence technology, and a good sentence similarity algorithm can improve the accuracy of questions and answers. In this study, we focus on the customer service in financial securities and propose a multi-feature fusion-based sentence similarity algorithm model, which improves the intelligence of customer service. Matrix splicing is adopted to fuse the morphological and semantic features of user question sentences and knowledge base sentences. The morphological features include N-gram similarity, edit distance, and Jaccard similarity. To extract semantic features, we propose a multi-head attention based neural network model named LBMA. With these fused features, a machine learning classifier is used to determine whether two sentences are similar, and the classification result of the classifier is regarded as the calculation result of the multi-feature fusion model. On the premise of not changing the semantic information as much as possible, the data set is expanded through data augmentation to improve the generalization of the model. Experimental results show that the proposed model can calculate the similarity of customer service data sets at an accuracy of 94.69%, higher than that of existing methods.