Aiming at the time-consuming, laborious, and weak expansibility of user intention recognition in question answering robots based on template matching, keyword co-occurrence or artificial feature set, this study proposes a model based on the combination of grid memory network (LSTM+CRF+Lattice) and Convolutional Neural Network (CNN) combined with the characteristics of geological literature question answering. In this hybrid model, users’ query intention recognition is regarded as a classification problem. Firstly, the grid memory network is used to identify the named entity and extract the relationship of the text information, then the CNN is used to classify the attributes of other text information input by users, and then the classification results are transformed into a structured way to meet the query of knowledge graph, and finally realizes the attribute mapping of user intention recognition. Experiments show that it is very helpful to improve the accuracy rate when considering the characteristics of geological knowledge.