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The large-scale and super-strength development of mineral resources in mining cities in long term has made great
contributions to China's economic construction and development, but it has caused serious damage to the ecological
environment even ecological imbalance at the same time because the neglect of the environmental impact even to the
expense of the environment to some extent. In this study, according to the characteristics of mining cities, the scientific
and practical eco-environmental vulnerability evaluation index system of mining cities had been established. Taking
Panzhihua city of Sichuan province as an example, using remote sensing and GIS technology, applying various types of
remote sensing image (TM, SPOT5, IKONOS) and Statistical data, the ecological environment evaluation data of
mining cities was extracted effectively. For the non-linear relationship between the evaluation indexes and the degree of
eco-environmental vulnerability in mining cities, this study innovative took the evaluation of eco-environmental
vulnerability of the study area by using artificial neural network whose training used SCE-UA algorithm that well
overcome the slow learning and difficult convergence of traditional neural network algorithm. The results of ecoenvironmental
vulnerability evaluation of the study area were objective, reasonable and the credibility was high. The
results showed that the area distribution of five eco-environmental vulnerability grade types was basically normal, and
the overall ecological environment situation of Panzhihua city was in the middle level, the degree of eco-environmental
vulnerability in the south was higher than the north, and mining activities were dominant factors to cause ecoenvironmental
damage and eco-environmental Vulnerability. In this study, a comprehensive theory and technology
system of regional eco-environmental vulnerability evaluation which included the establishment of eco-environmental
vulnerability evaluation index system, processing of evaluation data and establishing of evaluation model. New ideas and
methods had provided for eco-environmental vulnerability of mining cities.
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