This paper puts forward a new approach for analyzing the problems of urban public management based on the data collected from “12345” citizen service hotline. The research sets up the geocoding by the extraction of the spatial information from the call logs of Sanya City, Hainan Province. Meanwhile, the characteristics of time, space and categories of inquiries are documented and depicted by combining the original information of hotline records with the geocodes. By using high frequency word distribution and correlation network, the research reveals the major problems of cities, and analyzes the featured spatial-temporal distribution patterns of corresponding problems. The factors which affect the occurrence of urban problems are discussed with the combination of other urban characteristics including various types of point of interest (POI) representing urban functions and road network representing urban structure. The results are as follows. 1) The frequency of calls fluctuates on a weekly basis, and the total number of records in midweeks is larger than that of the weekends. 2) The hottest five categories of urban problems are noise, construction, communication and network, urban water use and parking issues, among which the noise and construction problems are closely correlated. 3) Different urban problems undergo distinct temporal patterns, while all of them are distributed in urban activity-intensive areas. 4) The occurrence of urban problems is not significantly relative with the density of road network, but positively relative with the POI densities of public facilities, transportation facilities, and educational and academic facilities, and significantly negative with the POI densities of scenic spots, sports and entertaining utilities, and administrative offices. The results demonstrate the effectiveness of “12345” citizen service hotline in urban problem discovery and depiction. Furthermore, this paper proposes several pathways for the optimization of urban governance using big data. For instance, the accumulation and analysis of urban data, the integration of multi-source data, and the enhancement of the degree of data share should be seriously appreciated. It’s necessary to utilize these data to support the fine urban governance and promote the construction of smart city.