[Objective] An opinion reason sentences classification model is proposed to mine the opinion reason sentences of reviews in online booking platform. [Methods] Firstly, a pretraining corpus containing millions of online reviews is constructed and an ORSC dataset is manually annotated to test the proposed model. Subsequently, the text features of ORSC dataset are extracted by adding the constructed corpus to ERNIE model. Finally, the BiLSTM model is used to merge all the features and identify the reviews containing opinion reasons. [Results] On ORSC datasets, the DERNIE model have reached an accuracy of 91.33% and a F1 value of 91.20%, after BiLSTM fusion features, the accuracy is improved to 94.57% and the F1 value is improved to 94.62%. [Limitations] The pre-trained language models require a large amount of data in the additional corpus, which will affect the computational speed and efficiency. [Conclusions] The features extraction and fusion method based on DERNIE-BiLSTM model can mine opinion reason sentences in online reviews more accurately.