The invention discloses an automatic question-answering method based on deep learning, and aims to provide an algorithm-based fully-automatic question-answering scheme for a user. According to the method, question and answer pairs crawled from websites are used as data sources, and questions with more complicated forms can be answered. According to the method, on the basis of traditional similar-question retrieval, a BOW model, a TFIDF model and a Word2Vec model are utilized to represent text contents of questions as vectors, similar questions are resorted and screened out through calculatingsimilarity between vectors, semantic knowledge can be introduced, the semantic gulf problem in traditional question retrieval processes can be solved, and validity of candidate answers can be improved. In addition, based on deep learning, the method utilizes a neural network model, which is obtained by training, for matching scoring on a question and the candidate answers, and can automatically extract high-layer matching features between the question and the answers, automatically give an answer of the question, improve accuracy of an automatic question-answering system, reduce manual intervention at the same time, and reduce system development costs.