Joint multi-task learning with knowledge graph embedding and semantic-enhanced encoding for teaching evaluation text analysis

Abstract

To address the difficulty of accurately analyzing large-scale unstructured teaching evaluation texts, this study proposes a teaching evaluation text analysis framework integrating knowledge graph embedding, semantic-enhanced encoding, and joint multi-task learning. Based on approximately 30,000 course evaluation records, a teaching evaluation knowledge graph containing about 8,000 entity nodes and 35,000 relation edges is constructed to model heterogeneous associations among courses, teachers, evaluation aspects, and labels. A semantic-enhanced multi-channel encoding mechanism is then designed to fuse contextual text representations, graph embeddings, and structural features into a shared representation space. On this basis, a joint multi-task learning framework is developed to simultaneously perform sentiment polarity classification, evaluation aspect classification, and satisfaction regression. Experimental results on real university data show that the proposed model achieves an Accuracy of 0.915 in sentiment polarity classification and 0.886 in evaluation aspect classification, with Macro F1 improving by approximately 1.4 and 2.1 percentage points, respectively, compared with TF-IDF + SVM and text-only deep models. In addition, the deployed system maintains an average response time of about 295 ms and a QPS of approximately 200 under 200 concurrent requests, demonstrating good engineering efficiency and robustness.