As the retrieval-based question and answer (Q&A) technology becomes increasingly mature, determining how to effectively use existing models and retrieval tools to achieve overall optimization of Q&A systems is a practical problem that needs to be studied. The study proposes a three-stage and a fusion of feature and representation problem retrieval model (TSFR-RM) for constructing intelligent customer service Q&A systems. Firstly, the similarity between the text representations of users’ questions and questions in knowledge bases is calculated by deep learning methods to target the top-k candidate answer set and give the model the ability of generalized retrieval. Secondly, multi-angle semantic features are constructed for pairs of answers to users’ questions and questions in knowledge bases to perform accurate comparison calculations. Finally, a state prediction model is built to return accurate answers to question retrieval. The experimental and practical application results show that the model improves the accuracy of the intelligent customer service retrieval system for a cultural tourism institution by up to 9.3 percentage points in the performance index of precision compared with other feature- and representation-based question retrieval models.