Point of interest auto-completion (POI-AC) is a featured function in the search engine of many Web mapping services. This function keeps suggesting a dynamic list of POIs as a user types each character, and it can dramatically save the effort of typing, which is quite, useful on mobile devices. Existing approaches on POI-AC for industrial use mainly adopt various learning to rank (LTR) models,with handcrafted features and even historically clicked POIs are,taken into account for personalization. However, these prior arts,tend to reach performance bottlenecks as both heuristic features,and search history of users cannot directly model personal input, habits. In this paper, we present an end-to-end neural-based framework for POI-AC, which has been recently deployed in the search,engine of Baidu Maps, one of the largest Web mapping applications with hundreds of millions monthly active users worldwide.