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After the implementation of two-day weekends policy, the change of people¡¦s recreational travels has resulted in an increasing trend of leisure activity. Since Taiwan joined WTO in 2002, the agricultural industry was the first to face fierce competition. This further led to a transformation of the agricultural industry in Taiwan ¡V the recreational farm has brought a new opportunity for Taiwan¡¦s agricultural industry, promoted the flourishing development of the recreational tourism in Taiwan, and even increased the farmers¡¦ economic income. This study mainly targeted on the tourists of the Maolin District Activity Center of Fengshan Farmers¡¦ Association in Maolin Village of Kaohsiung County. Based on the structure of the ¡§E-K-B Consumer Behavior Model¡¨ proposed by Engel, Blackwell and Kollat (1984), this study further discussed whether the motivation of travel, information search and selection criteria varied significantly among different consumer groups to provide references for the center for improvement as well as increasing management efficiency and profits. This study conducted an in-depth interview with the consumers at the center during weekends and obtained the primary data through survey-questionnaire method. 222 questionnaires were distributed and 219 valid questionnaires were collected. After the statistical data analysis, the findings are as the followings: 1. Structural analysis of the interviewees¡¦ basic information: according to the survey sample, most of the tourists aged between 20~49; the majority of the tourists are ¡§married¡¨ and have children above 6 years old and an ¡§university¡¨ degree; individual income mainly ranged from 20,000 to 30,000 dollars; most of them live in southern cities or counties which indicated the distance is a convenient advantage for southern residences to decide to go to the Maolin District Activity Center. 2. Analysis of tourists¡¦ behavior: over the past two years, most of the tourists have been to the farm once and stayed for less than 2 days; in average, each person has spent less than 3000 dollars. They went to the farm because of the purple milkweed butterflies. This indicated that the Maolin District Activity Center has taken the advantage of Maolin village¡¦s special resources ¡§Purple Milkweed Butterflies¡¨ to attract tourists. 3. The statistic variable of tourist population (age, education degree and income etc¡K) is significantly different in terms of the information search behavior. 4. The statistic variable of tourist population has a significant difference concerning about the selection criteria. 5. The statistic variable of tourist population has a significant difference in terms of the motivation of travel. 6. The tourists¡¦ motivation of travel is significantly positively correlated with the information search behavior and selection criteria.

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With the advance of computer science, the processing power and speed of computer increase dramatically. Such improvement also allows Artificial intelligence a feasible tool to assist on dealing with complex problem or scenario. In recent year, the neural network has become an important methodology in Artificial intelligence technology field. It is capable of producing good referential materials for categorizing and predicting financial crisis rights worth.After Financial Crisis, many unhealthy companies busted out emerging financial crisis and led to the crash of the stock market. For our study, we try to build early warning System (EWS) for predicting financial distress. In out research, a neural network to categorized Self-Organizing Map and combine with predicted Back-Propagation Neural Networks to produce Hybrid-Neural Networks Forecast Model is applied. This study also compares and checks Hybrid-Neural Networks Forecast Model with simple Back-Propagation Neural Networks model. The predicting models in this investigation employed 16 financial variables, selected in previous research on financial distress, as input variables. For our results pointed out that the accuracy rate with the predicting model of self-organizing map neural network combined with Back-propagation neural network was much better than the control group which only adopted back-propagation neural network models. The thesis attempts to apply clustering technique, which to grasp the environmental changes, to make a dynamic learning and to provide investors more explicit company information to support decision-makers to do the correct choice. key word: Self-organizing map¡B backpropagation neural network¡B financial distress prediction model

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