As an important indicator reflecting urban ecological environment, urban ecological livability has aroused academia concerns. However, existing studies mainly focused on city-scale or region-scale ecological livability evaluation. With the increasing demands for fine-scale urban planning and management, small-scale livability evaluation is urgently needed, however, it is still conceptual and qualitative. In recent years, the availability of abundant, fine-grained, and multi-source data in the big data era laid the foundation for comprehensive and quantitative livability evaluation at much finer scales. In this study, we propose a framework to evaluate fine-scale livability using multi-source data. Firstly, we combined remotely sensed imageries and Point-of-interests (POI) data to generate multiple features. Subsequently, these features were represented via Latent Dirichlet Allocation (LDA) model. Random Forest model was then utilized to extract dwelling areas, namely, the basic evaluating units. Next, TOPSIS model was employed for evaluating ecological livability, by integrating indicators derived from the multi-source data. We also validated the developed methods in Shenzhen City. The results show that:(1) the accurate urban functional map was obtained by combining multisource data and machine learning methods with overall accuracy of 82.1%. Most of the residential zones and urban villages in Shenzhen City were distributed within the central and southern urban areas. (2) The TOPSIS can be used for quantitative evaluation of the ecological livability of the dwelling areas. Most of the dwelling areas with high scores had high proportion of green space, good air quality and low building density. while the opposite sides were mainly concentrated in urban villages. The score of urban ecological livability is consistent with actual conditions. The results proved the effectiveness of the five indicators in evaluating livability of Shenzhen City. However, the thermal comfort indicator which was reflected by Land Surface Temperature had a weak influence on livability score in our study. (3) The spatial distribution of dwelling areas is quite different over different district, as well as their livability scores. Nanshan, Futian and Luohu districts have more residential areas than others. Some districts (such as Baoan, Longhua and Longgang) have contrasting scores, because they have both many high-score residential areas and low-score urban villages. While Pingshan, Yantian and Dapeng districts receive higher ecological livability scores than others, due to the limited dwelling areas, more green space, and better air quality. The experimental results of this case study demonstrated the proposed study framework is effective and reasonable. These results can not only provide theoretical support for improving urban living environment, but also provide technical supports for the ecological livability renovation projects and urban planning.