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Phenotypic data analysis based on image data and machine learning has become one of the important issues in interdisciplinary research. In recent years, the big data and deep learning techniques have provided powerful tools for image analysis and machine vision. Currently, the generative adversarial network is becoming a novel framework for the process estimation generation model. It can generate highquality image data and provide an effective approach for solving the problem of small sample data and unbalanced data analysis and so on. As one of the important fungi, mushroom has a plenty of varieties and the long tail distribution and nonequilibrium of the data distribution bring great difficulties to its phenotypic intelligent classification and identification. Aiming to design a highefficiency mushroom phenotyperesistance network MPGAN with mushroom phenotype data. The phenotypic data generation technology of mushroom was studied, and the generated confrontation network structure for mushroom phenotypic data generation was designed. The system was divided into two modules: model training and phenotypic image generation. To improve the quality of the generation, Wasserstein distances and loss functions with gradient penalty were used. Experiments were conducted on two datasets: open source data and private data sets, and results analysis were performed with the learning rate, number of batches required to process EPOCH and Wasserstein distances. The phenotypic data of the mushroom produced with this approach can furnish data basis for the classification of the mushroom data in the later stage, and provide solutions for solving the issues of unbalanced data and long tail distribution of the mushroom classification. The research can provide technical support for the study of high quality mushroom phenotypic data sets.