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With the continuous accumulation of users' check‐in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point‐of‐interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check‐in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users' check‐in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category‐aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check‐in data, capture long‐range dependence between user check‐ins and get better recommendation results of POI category. We combine the spatiotemporal information of check‐in data and take the POI category as users' …