The paper introduces a Recurrent Convolutional Neural Network (RCNN) for text classification, aiming to capture contextual information and improve the representation of text without relying on human-designed features. The RCNN combines a bidirectional recurrent structure to capture contextual information and a max-pooling layer to identify key components in the text. The model is evaluated on four datasets: 20Newsgroups, Fudan Set, ACL Anthology Network, and Sentiment Treebank. Experimental results show that the RCNN outperforms state-of-the-art methods, particularly in document-level datasets, demonstrating its effectiveness in text classification tasks. The RCNN's ability to capture long-distance patterns and its linear time complexity make it a competitive alternative to traditional methods and other neural network models.The paper introduces a Recurrent Convolutional Neural Network (RCNN) for text classification, aiming to capture contextual information and improve the representation of text without relying on human-designed features. The RCNN combines a bidirectional recurrent structure to capture contextual information and a max-pooling layer to identify key components in the text. The model is evaluated on four datasets: 20Newsgroups, Fudan Set, ACL Anthology Network, and Sentiment Treebank. Experimental results show that the RCNN outperforms state-of-the-art methods, particularly in document-level datasets, demonstrating its effectiveness in text classification tasks. The RCNN's ability to capture long-distance patterns and its linear time complexity make it a competitive alternative to traditional methods and other neural network models.