中文摘要：

本文以中国大陆31个省级行政区为研究对象，选取反映地区经济综合竞争力的17项重要指标，通过对原始数据的采集处理，运用人工神经网络（ANN）中的自组织映射网络（SOFM）及统计分析软件SPSS中的两种系统聚类分析方法对地区经济综合竞争力区域差异进行分类研究，最终将31个省、市、自治区的经济综合竞争力强弱划分为4类，即经济综合竞争力最强、经济综合竞争力较强、经济综合竞争力一般及经济综合竞争力较弱4类地区。研究表明：我国经济综合竞争力区域差异显著，主要表现为东部和中、西部及沿海和内地之间的差异。研究也表明运用人工神经网络和系统聚类分析方法进行分类研究可以相互检验分类结果，易于发现问题，提高分类的准确性，是一条具有发展和应用前景的途径。

英文摘要：

Comprehensive economic competitiveness is the ability of an area to attract resources and compete within a larger region, and results from the optimization of regional resource allocation based on economic development status and development potential. It is a reflection of economic strength, economic extroversion, financial environment, innovation factors, government management and science and technology development, and results from a combination of many kinds of economic variables and the resulting dynamics. This study is based on 31 provinces, cities, and autonomous regions in mainland China. We selected 17 indices related to regional comprehensive economic competitiveness, and applied Self-Organizing Feature Maps （SOFM） based on Artificial Neural Network methods and statistical analysis. The regions were divided into four types, ranging from the highest level of economic competitiveness to the lowest. The most competitive areas are Beiiing, Shanghai and Tianjin.The next level, considered to be strongly competitive, includes Guangdong, Jiangsu, Shandong and Zhejiang provinces. Medium-level competitiveness is characteristic of Liaoning, Fujian, Shanxi, Hubei, Sichuan, Hunan, Henan and Hebei. The weakest areas are Inner Mongolia, Heilongjiang, Jilin, Shaanxi, Chongqing, Anhui,Guangxi, Jiangxi, Hainan, Guizhou, Yunnan, Gansu, Qinghai, Ningxia, Xinjiang and Xizang. The research results indicate clear regional disparities of comprehensive economic competitiveness. The provinces which have strong competitiveness are mainly along the east coast of China, while the weaker provinces are mainly located in the central and western areas. The government at all levels should take effective measures to improve economic efficiency, strengthen support to less-developed areas and industries, and try to reduce regional disparities in economic competitiveness. The research hierarchical cluster identifying problems results also demonstrate that application of both artificial neural network and analysis can be used for compa