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.

In the field of Natural Language Processing （NLP）， as an efficient method for sentence representation learning， contrastive learning mitigates the anisotropy of Transformer-based pre-trained language models effectively and enhances the quality of sentence representations significantly. However， the existing research focuses on English conditions， especially under supervised settings. Due to the lack of labeled data， it is difficult to utilize contrastive learning effectively to obtain high-quality sentence representations in most non-English languages. To address this issue， a cross-lingual knowledge transfer method for contrastive learning models was proposed， transferring knowledge across languages by aligning the structures of different language representation spaces. Based on this， a simple and effective cross-lingual knowledge transfer framework， TransCSE， was developed to transfer the knowledge from supervised English contrastive learning models to non-English models. Through knowledge transfer experiments from English to six directions， including French， Arabic， Spanish， Turkish， and Chinese， knowledge was transferred successfully from the supervised contrastive learning model SimCSE （Simple contrastive learning of sentence embeddings） to the multilingual pre-trained language model mBERT （Multilingual Bidirectional Encoder Representations from Transformers） by TransCSE. Experimental results show that model trained using the TransCSE framework achieves accuracy improvements of 17.95 and 43.27 percentage points on XNLI （Cross-lingual Natural Language Inference） and STS （Semantic Textual Similarity） 2017 benchmark datasets， respectively， compared to the original mBERT， proving the effectiveness of TransCSE. Moreover， compared to cross-lingual knowledge transfer methods based on shared parameters and representation alignment， TransCSE has the best performance on both XNLI and STS 2017 benchmark datasets.