Understanding the demographics of hidden population, such as men who have sex with men (MSM), sex workers, or injecting drug users, are of great importance for the adequately deployment of intervention strategies and public health decision making. However, due to the hard-to-access properties, e.g., lack of a sampling frame, sensitivity issue, reporting error, etc., traditional survey methods are largely limited when studying such populations. With data extracted from the very active online community of MSM in China, in this study we adopt and develop location inferring methods to achieve a high-resolution mapping of users in this community at national level. The performances of popular inference algorithms are compared to elucidate the most suitable approach. In addition, we propose a new hybrid model, which is proven to achieve the highest accuracy for inferring locations of online users only based on text content. This method is conducive to overcoming the sparse location labeling problem in user profiles, and can be extended to the inference of geo-statistics for other hidden populations.