Abstract: In response to the cross-border integration, digitalization, and intelligentization characterizing the modern service sector, this paper introduces value network theory and develops a competitiveness evaluation framework for modern service enterprises across three dimensions: “value creation, value delivery, and value acquisition.” Large-scale data imputation is performed using machine learning combined with the SCIS method, enabling the identification and positioning of modern service enterprises, and resulting in an analysis dataset covering 580 000 enterprises, including 791 listed companies. For indicator weighting, a combined approach of the CRITIC method and entropy method is employed. The evaluation results indicate that although competitiveness in industries and their sub-sectors has improved in vertical comparisons, overall levels remain relatively low, and multidimensional imbalances exist across industries, within industries, and among indicators. The framework developed in this paper is applicable to the analysis of modern service enterprise competitiveness in China, offering valuable theoretical and practical guidance for relevant enterprises and policymakers.

Key words: modern service sector, competitiveness of enterprises, value network, data imputation, evaluation indicator system

摘要： 针对现代服务业呈现的跨界融合、数字化与智能化等特征，本文引入价值网络理论，从“价值创造、价值传递、价值获取”3个维度构建现代服务企业竞争力评价体系。通过机器学习与SCIS方法进行大规模数据补全，实现了对现代服务企业的识别与定位，并构建了一个包含58万家全样本企业（含791家上市公司）的分析数据集。在指标赋权方面，采用CRITIC法与熵值法相结合的方式确定权重。评价结果表明，虽然行业及其细分领域竞争力在纵向比较中有所提升，但整体仍处于较低水平，且存在行业间、行业内及指标间的多维失衡现象。本文所构建的框架适用于中国现代服务企业竞争力分析，为相关企业决策与政策制定提供了理论与实践参考。

关键词: 现代服务业, 企业竞争力, 价值网络, 数据补全, 评价指标体系