基于GCA-DEA-MSVC方法的高校科研平台评价预测研究

高校科研平台评价与预测分析是促进科研工作健康高效发展的重要载体，但数据指标繁冗、逻辑关系复杂、影响因素众多等大大加剧了科研平台运行评价和预测难度。本文从大数据角度出发探索一种基于GCA-DEA-MSVC方法的高校科研平台评价预测方法。首先利用GCA方法从科研平台运行数据库中挖掘、提取出与评价结果密切相关的关键特征指标并进行分类构建特征指标库，然后利用DEA方法对特征指标库数据进行融合，提升数据质量构建相对效率指标库；最后，将特征指标库与相对效率指标库再次融合，基于改进的MSVC方法构建了高效的科研平台运行状态评价分类预测模型，并利用教育部重点实验室评价数据开展了实验研究，验证了所提方法的有效性。

Study on Evaluation and Prediction of Scientific Research Platforms of Universities using a GCA-DEA-MSVC Methodology

The evaluation and prediction of the operation state of university scientific research platforms plays an important role in promoting the healthy and efficient development of scientific research work. However, the complexity of data indicators, complex logical relationships, and numerous influencing factors have greatly increased the difficulty. From the perspective of big data, a method based on GCA-DEA-MSVC is explored. First, the GCA method is used to mine and extract key feature indicators that are closely related to the evaluation results from the database and classify to build a feature indicator database. After that, the DEA method is used to fuse the feature index database data to improve the data quality and build a relative efficiency index database. Finally, the feature index library and the relative efficiency index library were re-fused, and an efficient classification and prediction model for the evaluation of the operating status of the scientific research platform is constructed based on the improved MSVC method. An experimental study is conducted using the evaluation data of the key laboratory of the Ministry of Education to verify the effectiveness of the proposed method.