Mixed sampling data, whose different features are collected at different sampling frequencies, pervasively exists in the real world. Because the different sampling data set not only has different features, but also has different number of samples, traditional classification methods cannot be used directly. Therefore, this paper proposes a classification method for mixed sampling data based on Fisher discrimination dictionary learning to solve the classification problem of mixed sampling data. Inspired on some classification ideas for multi-view data, the proposed model compares multiple data collected at multiple sampling frequencies to multiple views of multi-view data, and designs a way to learn a sub-dictionary for each class of each view. A structured dictionary whose dictionary atoms have correspondence to the class labels is learned, so that the within-class scatter is less and the between-class scatter is bigger based on the Fisher discrimination criterion. In addition, this paper designs a specific classification scheme for the inconsistent sample size of mixed sampling data. Finally, experimental results demonstrate the effectiveness of the proposed model.