To solve the polysemy troublesome problem of tourism text in feature extraction, a Chinese scenic spot named entity recognition model based on fusion language model is studied for the problem of attraction alias in the visual recognition of tourist travel texts. Firstly, the BERT is used for tourism text feature extraction to obtain the word granularity vector matrix. BiLSTM is used to extract the context information. The CRF is used to obtain the global optimal sequence, and finally the tourist attraction entity is obtained. Experiments show that the performance of the proposed model is significantly improved. In the test of scenic spot identification in the actual tourism field, compared with the previous research, precision and recall rates are increased by 8.33% and 1.71%, respectively.