The discovery of community structures using urban big data is an important research direction in urban computing. Effective representation of the structural characteristics of the communities in the "15-minute living circle" can be used to evaluate the facilities around the living circle communities in a fine-grained manner， which is conducive to urban planning as well as the construction and creation of a livable living environment. Firstly， the urban community structure oriented to "15-minute living circle" was defined， and the structural characteristics of the living circle communities were obtained by representation learning method. Then， the embedding representation framework of the living circle community structure was proposed， in which the relationship between the Points Of Interest （POI） and the residential area was determined by using the travel trajectory data of the residents， and a dynamic activity map reflecting the travel rules of the residents at different times was constructed. Finally， the representation learning to the constructed dynamic activity map was performed by an auto-encoder to obtain the vector representations of the potential characteristics of the communities in the living circle， thus effectively summarizing the community structure formed by the residents’ daily activities. Experimental evaluations were conducted using real datasets for applications such as community convenience evaluation and similarity metrics in living circles. The results show that the daily latent feature expression method based on POI categories is better than the weekly latent feature expression method. Compared to the latter， the minimum increase of Normalized Discounted Cumulative Gain （NDCG） of the former is 24.28% and the maximum increase of NDCG is 60.71%， which verifies the effectiveness of the proposed method.