With the development of the economy, most cities will expand continuously to the surrounding areas, thus leading to the emergence of urban fringe areas with both urban and rural characteristics. The urban fringe area, located between urban and rural areas, is the most intense area of urban land use change and one of the most likely areas for urban construction land expansion in the future. How to identify urban fringe accurately and quantitatively is of great significance for urban planning and sustainable land use. However, most existing methods about the delineation of urban fringe area is just based on one or one type of indicators, and the judgment result is too fragmented to reflect the continuity of the urban spatial structure. What's more, the urban preset boundary range, the water body and the urban green space have great interference with the judgment results of urban fringe. In view of the above problems and from multi-perspective of nature, population and social economy, this paper defines urban fringe based on deep learning and multi-source data (remote sensing image, population density and POI big data). Furthermore, the proposed method has been used to detect the urban fringe area of Guangzhou city in our experiments. The results show that: (1) This method can divide the city into urban core area, urban fringe and rural area accurately without the impact of the preset boundary range. Eventually, this way can eliminate the fragmentation caused by the internal water and green space of urban areas. (2) The results of urban fringe area are well coupled with the road network. Network distribution of the urban core area is densest, followed by the urban fringe area. (3) The spatial distribution of urban core area of Guangzhou from the experiments is reasonable and consistent with the actual situation. All in all, the proposed method can consider comprehensively multi- perspective factors and detect urban fringe effectively, thus can provide better guidance for formulation of policies for urban development, such as urban planning, sustainable development, and urban statistical analysis.