With the continuous development of the digital financial service industry， the Internet and financial service systems have accumulated a large amount of text data. The automatic classification of financial events described in the financial text is a realistic demand of financial technology， and also a widespread concern in the field of natural language processing and machine learning. At present， the deep learning method has been widely used in text classification. Addressing the issues of lack of labeled data in multi label classification of financial events in text data， frequent resource consumption of existing deep learning methods， and failure to explore the specific characteristics of financial event texts， a semi-supervised multi-label classification method of financial events is proposed by using ALBERT， TextCNN and other presentation tools， introducing the subject word attention mechanism. Firstly， the problem of insufficient labeled data is alleviated through unsupervised data augmentation （UDA） methods； Secondly， the subject word attention mechanism is introduced， and the ALBERT dynamic word vector representation method is used to represent the words in the text； Then， TextCNN is used to represent the text comprehensively； Finally， cross entropy and KL divergence are used to measure the loss of labeled data and unlabeled data to train the model. The effectiveness of the proposed method is verified on the financial text dataset.