With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone. However, text information and structure information reflect different aspects of a user. An organic combination of them is beneficial to mining user behavior patterns, thus help identify users across platforms accurately. The challenging problems are the effective representation and similarity computation of the text and structure information. We propose a mapping method which integrates text and structure information. At first, the model represents user name, description, location information based on word2vec or string matching, and friend information represented as relation network is regarded as structure information. Then these information are used for similarity computation using Jaccard index or cosine similarity. After similarity computation, a linear model is adopted to get the overall similarity of user pairs to perform user mapping. Based on the proposed method, we develop a prototype system, which allows users to set and adjust the weights of different information, or set expected index. The experimental results on a real-world dataset demonstrate the efficiency of the proposed model.