Current main stream models cannot fully express the semantics of question and answer pairs， do not fully consider the relationships between the topic information of question and answer pairs， and the activation function has the problem of soft saturation， which affect the overall performance of the model. To solve these problems， an answer selection model based on pooling and feature combination enhanced BERT （Bi-directional Encoder Representations from Transformers） was proposed. Firstly， adversarial samples and pooling operation were introduced to represent the semantics of question and answer pairs based on the pre-training model BERT. Secondly， the relationships between topic information of question and answer pairs were strengthened by the feature combination of topic information. Finally， the activation function in the hidden layer was improved， and the splicing vector was used to complete the answer selection task through the hidden layer and classifier. Model validation was performed on datasets SemEval-2016CQA and SemEval-2017CQA. The results show that compared with tBERT model， the proposed model has the accuracy increased by 3.1 percentage points and 2.2 percentage points respectively， F1 score increased by 2.0 percentage points and 3.1 percentage points respectively. It can be seen that the comprehensive effect of the proposed model on the answer selection task is effectively improved， and both of the accuracy and F1 score of the model are better than those of the model for comparison.