The current mainstream neural networks cannot satisfy the full expression of sentences and the full information interaction between sentences at the same time when processing answer selection tasks. In order to solve the problems， an answer selection model based on Dynamic Attention and Multi-Perspective Matching （DAMPM） was proposed. Firstly， the pre-trained Embeddings from Language Models （ELMo） was introduced to obtain the word vectors containing simple semantic information. Secondly， the filtering mechanism was used in the attention layer to remove the noise in the sentences effectively， so that the sentence representation of question and answer sentences was obtained in a better way. Thirdly， the multiple matching strategies were introduced in the matching layer at the same time to complete the information interaction between sentence vectors. Then， the sentence vectors output from the matching layer were spliced by the Bidirectional Long Short-Term Memory （BiLSTM） network. Finally， the similarity of splicing vectors was calculated by a classifier， and the semantic correlation between question and answer sentences was acquired. The experimental results on the Text REtrieval Conference Question Answering （TRECQA） dataset show that， compared with the Dynamic-Clip Attention Network （DCAN） method， which is one of the comparison aggregation framework based baseline models， the proposed DAMPM improves the Mean Average Precision （MAP） and Mean Reciprocal Rank （MRR） both by 1.6 percentage points. The experimental results on the Wiki Question Answering （WikiQA） dataset show that， the two performance indices of DAMPM is 0.7 percentage points and 0.8 percentage points higher than those of DCAN respectively. The proposed DAMPM has better performance than the methods in the baseline models in general.
