The automatic classification of point of interest (POI) function types based on POI name texts and intelligent computing can provide convenience in travel recommendations, map information queries, urban function divisions, and other services. However, POI name texts belong to short texts, which few characters and sparse features. Therefore, it is difficult to guarantee the feature learning ability and classification effect of the model when distinguishing the POI function types. This paper proposes a POI classification method based on feature extension and deep learning to establish a short-text classification model. We utilize an Internet search engine as an external knowledge base to introduce real-time, large-scale text feature information to the original POI text to solve the limitation of sparse POI name text features. The input text information is represented by the attention calculation matrix used to reduce the noise information of the extended text and the word-embedding matrix of the original text. We utilize a convolutional neural network with excellent local feature extraction ability to establish the classification model. Experimental results on a real-world dataset (obtained from Baidu) show the excellent performance of our model in POI classification tasks compared with other baseline models.

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