Streamline rendering has long remained as one of the most common techniques for flow visualization. The streamline is an effective sparse representation of the flow field, which can capture the flow behavior, but generating streamline needs long-term particle tracing and massive integral operations. Large-scale flow visualization takes considerable computation time, and the parallel computing algorithm and high-performance equipment are needed. In this study, a high-resolution streamline generation algorithm based on deep learning is designed. The initial sparse low-resolution streamline is quickly mapped into the dense high-resolution streamline to provide reliable streamline visualization results in a short time. On this basis, an interactive real-time flow visualization system is developed, which is capable of flow-field feature detection, attribute correlation analysis, information theory analysis, etc. It can help users quickly understand the flow field data and find their areas of interest for post-hoc analysis, avoiding redundant data and enhancing work efficiency. In addition, it can meet the users’ needs for multi-dimensional correlation analysis of flow field structures, features, and attributes.