Abstract:To solve the scarcity of text classification algorithms in finance and the inability of existing algorithms to adequately extract word-to-word relations, long-distance dependency, and deep feature information in texts, this study proposes a text depth relationship extraction algorithm based on improved convolutional self-attention model. The algorithm introduces self-attention in a modified deep pyramidal convolutional neural network (DPCNN) and builds a text classification model jointly with bi-directional gated neural network (BiGRU) module to solve the problem of extracting long-distance dependency feature information and word-to-word relationship feature information for long texts in finance. Then the joint extraction function of deep feature information and contextual semantic information in texts is realized. Experiments on THUCNews short text and long text datasets show that the proposed method has significant improvement in evaluation indexes compared with BERT and other methods. The comparison experiments on the dataset of homemade financial long texts show that the accuracy and F1 value of the algorithm model are higher compared with other models. A series of experiments demonstrate that the algorithmic model can perform the classification task against financial long texts more accurately.