Linking user accounts belonging to the same user across different platforms with location data has received significant attention, due to the popularization of GPS-enabled devices and the wide range of applications benefiting from user account linkage (e.g., cross-platform user profiling and recommendation). Different from most existing studies which only focus on user account linkage across two platforms, we propose a novel model ULMP (i.e., user account linkage across multiple platforms), with the goal of effectively and efficiently linking user accounts across multiple platforms with location data. Despite of the practical significance brought by successful user linkage across multiple platforms, this task is very challenging compared with the ones across two platforms. The major challenge lies in the fact that the number of user combinations shows an explosive growth with the increase of the number of platforms. To tackle the problem, a novel method GTkNN is first proposed to prune the search space by efficiently retrieving top-k candidate user accounts indexed with well-designed spatial and temporal index structures. Then, in the pruned space, a match score based on kernel density estimation combining both spatial and temporal information is designed to retrieve the linked user accounts. The extensive experiments conducted on four real-world datasets demonstrate the superiority of the proposed model ULMP in terms of both effectiveness and efficiency compared with the state-of-art methods.