With the popularity and development of Internet, people like to take part in multiple social networks to enjoy different kinds of services. Consequently, an important task is to identify users in the networks, which is helpful for user recommendation, behavior analysis and impact maximization. Most state-of-the-art works on this issue are mainly based on the user's structure features and attribute features. They prefer to exploit user's local features and are limited by the number of the known matching users. In this paper, a method based on global view features is proposed to align users with crowdsourcing (OCSA). First, crowdsourcing is used to increase the number of known matching users on networks. Then, global view features are used to evaluate the similarity between users to improve the accuracy of user identification. Finally, an iterative two-stage matching method is put forward to answer the user identification. The results of experiments show that the presented method has better performance on precision and recall, especially when the number of known matching users is insufficient.