国家自然科学基金（62172351，61728204）；高安全系统的软件开发与验证技术工业和信息化部重点实验室（NJ2018014）；中国学位与研究生教育学会资助（B-2017Y0904-162）；华为创新DB IRP项目（CCF-HUAWEI DBIR2020001A）.
As the core problem of text mining, text classification task has become an essential issue in the field of natural language processing. Short text classification is a hot-spot topic, which many problems are urgent to be solved in text classification due to its sparseness, real-time and non-standard characteristics. In certain specific scenarios, short texts have many implicit semantics, which brings challenges to tasks such as mining implicit semantic features in limited texts. The existing research methods mainly apply traditional machine learning or deep learning algorithms for short text classification. However, this series of algorithm is complex and requires enormous cost to build an effective model, meanwhile, the algorithms are not efficient. In addition, short text contains less effective information and abundant colloquial language, which requires a stronger feature learning ability of the model. In response to the above problems, we propose the KAeRCNN model based on the TextRCNN model, which combines knowledge aware and the dual attention mechanism. We construct the knowledge-aware in two parts, which includes the stage of knowledge graph entity linking and knowledge graph embedding. As external knowledge can be introduced to obtain semantic features. At the same time, the dual attention mechanism can improve the model's efficiency in extracting effective information from short texts. Excessive experimental results show that the KAeRCNN model proposed in this paper is significantly better than traditional machine learning algorithms in terms of classification accuracy, the F1 score, and practical application effects. We further verified the performance and adaptability of the algorithm with different datasets. The accuracy rate of our approach reached 95.54%, and the F1 score reached 0.901. Compared with the four traditional machine learning algorithms, the accuracy rate is increased by about 14% on average, and the F1 score is increased by about 13%. Compared with TextRCNN, the KAeRCNN model improves accuracy by about 3%. In addition, the experimental results of comparison with deep learning algorithms also show that our model has better performance in classification of short text from other fields. Both theoretical and experimental results indicate that the KAeRCNN model proposed in this paper is effective for short text classification.