Abstract: The clustering analysis of tourist market plays a lead role in the exploitation of touring market. Until now, although the quantitative methods are usually thought to be more dependable, more qualitative methods have been used to the clustering of tourist market due to the complexity of the behaving system of tourists. Artificial neural networks, simulating some characteristics of human brain, are proved to be effective models to the analysis of complex system. They are composed of many simple intercommunicating neurons that work in parallel to solve a special problem. They are much faster and effective than most conventional methods, especially for complicated behavior system that are impacted by all kinds of different factors, for once a network has been set up, it can learn in a self-organizing way which has much common in the mimic simple biological nervous system. In this paper, a soundly trained self-organizing feature map (SOFM) is employed to the clustering of tourist market of the Wuyishan, a famous scenic spot in the Fujian province, the southeast of China. First, concerning the available data, 11 variables that have influence on the demand of tourists are selected. Second, through the factor analysis, those 11 variables are compressed to 3 orthogonal factors. Third, useing these 3 factors as input variables, a SOFM is set up. When the neural network is trained appropriately, it classifies the data sets. The results show that 31 provinces or autonomous regions are classified into 3 groups, which is consistent with the results of the questionnaire survey in Wuyishan scenic spot. The results also indicate that SOFM is an alternative effective quantitative approach in clustering of tourist market.

Key words: artificial neural network, clustering of tourist market, Wuyishan, factor analysis

摘要： 客源市场分区对于旅游营销决策具有很重要的指导作用，目前对客源市场的分区缺乏定量化的方法。以武夷山市为例，用人工神经网络中的SOFM网络对其国内客源市场分区进行了研究，通过对影响旅游需求的11个变量进行因子分析，将得到的3个因子为输入变量，将国内客源市场分为3个区，结果与在武夷山景区进行问卷调查得到的结果基本一致，研究方法对国内其他类似旅游景区开发具有显著的借鉴意义。

关键词: 人工神经网络, 客源市场分区, 武夷山, 因子分析