The existing Automated Essay Scoring （AES） methods based on Pre-trained Language Model （PLM） tend to use global semantic features extracted from PLM directly to represent essay quality， while neglecting the associations between essay quality and more fine-grained features. In order to solve the problem， focused on Chinese AES research， the quality of essays was analyzed and evaluated from various textual perspectives， and a Chinese AES method was proposed by learning multi-scale essay features jointly using Graph Neural Network （GNN）. Firstly， discourse features at both the sentence level and paragraph level were extracted by utilizing GNN. Then， joint feature learning was performed on these discourse features and the global semantic features of essays， so as to achieve more accurate scoring of essays. Finally， a Chinese AES dataset was constructed to provide a data foundation for Chinese AES research. Experimental results on the constructed dataset show that the proposed method has an improvement of 1.1 percentage points in average Quadratic Weighted Kappa （QWK） coefficient across six essay topics compared to R2-BERT（Bidirectional Encoder Representations from Transformers model with Regression and Ranking）， validating the effectiveness of joint multi-scale feature learning in AES tasks. Meanwhile， ablation experimental results further demonstrate the contribution of essay features at different scales to scoring effect. To prove the superiority of small models in specific task scenarios， a comparison was conducted with the currently popular large language models， GPT-3.5-turbo and DeepSeek-V3. The results show that BERT （Bidirectional Encoder Representations from Transformers） model using the proposed method has the average QWK across six essay topics 65.8 and 45.3 percentage points higher than GPT-3.5-turbo and DeepSeek-V3， respectively， validating the observation that Large Language Models （LLMs） underperform in domain-specific discourse-level essay scoring tasks due to the lack of large-scale supervised fine-tuning data.