Set data appears in various application scenarios, such as point cloud data in autonomous driving and flow cytometry data in the medical field. Set Transformer combines attention mechanism and sparse induction point learning to propose a set data processing network with linear computational complexity, which is used for tasks such as point cloud object classification and abnormal flow cytometry data detection. However, the sparsity of inducing points, proposed in Set Transformer, makes it challenging for the network to learn the ability to encode the global structure of set data directly. This article proposes the Synchronous Encoding Cross Attention module and the Composite Inducing Points, which work together to address this issue. Firstly, the proposed Synchronous Encoding Cross Attention module encodes the set data while encoding the induction points, thereby filtering out interference information and enhancing the learning of set data global structural information in the subsequent cross attention. Secondly, we use composite inducing points to maintain the consistency of global structural information during the encoding process. Finally, we validate the effectiveness of the proposed methods in four application scenarios through comprehensive comparisons.