Accurate identification of microseismic events is the basis of data processing in microseismic monitoring. To address the issue of low accuracy in identifying microseismic events using deep learning methods, this paper firstly constructs a basic semi-supervised Generative Adversarial Network (GAN) classification model based on downhole microseismic monitoring data. The model consists of a generator for simulating the distribution of real data and a discriminator for identifying microseismic events. Next, layer normalization is introduced to reduce the training loss of the discriminator. Meanwhile, a convolutional interpolation method is applied to the generator to improve its ability of autonomously learning and extracting detailed signal features. In order to verify the effectiveness of the proposed method, actual microseismic data from fracturing monitoring is used as the dataset for training and testing the model. Experimental results indicate that the identification method based on semi-supervised GAN outperforms the identification method based on convolutional neural network in terms of accuracy and precision. Compared with the latter model, the former model has faster convergence and more stable training results. The accuracy of the test set for the improved semi-supervised GAN identification model can reach 97%, and all the test indicators of this model have been improved. The improved method can better learn the shape features of microseismic events, effectively identifying microseismic event samples, which increases the identification rate of microseismic events based on neural network classification models.