Abstract: As ecotoursim develops rapidly in China, it becomes very challenging to find the right balance between conserving the environment and meeting the growing demand of tourism, i.e., how to provide the best possible experience to the tourists ensuring the sustainable development of the environment. Hence, it is imperative to conduct comprehensive research on ecotourist. Ecotourist study must deperd on enough ecotourist samples. Therefore, it is important to identify ecotourists from visitors to protected areas.The majority of previous studies on ecotourism have used a behavioral approach to the identification of ecotourists. While these studies provide a number of useful ecotourist profiles, they do not provide a general scale in identifying ecotourists across a wide array of contexts. The paper adopted an integrated classification technique comprising K-Nearest Neighbor (KNN) and Back-Propagation (BP) Networks to identify the ectourists and 423 persons who answered the questionnaires during the National Day holiday in 2003 in Baihua Mountain Nature Reserve of Beijing and 139 out of them were identified as ecotourists. Based on the studies of the characteristics of the ecotourists and general tourists in Baihua Mountain and progress of the interrelated research in foreign countries, we set a new ecotourists classification index: Ecotourism Interest (EI). EI describes the extent that the tourists are interested in the ecotourism product, together with NEP (New Ecological Paradigm) and VIS (the number of times per visitor visiting rural natural regions), which constitutes the most important characteristic vectors for the identification and segmentation of ecotourists. Applying the same identified method again on the tourists in Baihua Mountain with these three vectors, the accurate identification rate to ecotourists is as high as 87.1% and the rate to all tourists is as high as 80.1%. At the same time, Logistic regression models are also used to test the hypotheses and the accurate identification rate to all tourists is as high as 82.7%. The classification effect with EI and VIS as eigenvectors is close to the effect with NEP and VIS as eigenvectors. However, the effect with EI, NEP and VIS as eigenvectors is better than with EI and VIS or NEP and VIS. Therefore, empirical results show that the ecotourism interest scale is useful in identifying if tourists are ecotourists. However, EI’s effect to the identification is lower than other two vectors at present because the development of ecotourism in China is still in the early phase and the differences between the ecotourists and non-ecotoursits are not very distinct. However, with the development of ecotourism in China, the ecotourists will have more representative motivation characteristics. At that time EI’s effect to the identification would more important. On the whole, EI appears to offer a useful explanation of the tourist participation in ecotourism activities. Other factors, such as age, gender, income, party composition, and organization of travel, influenced choice of the ecotourism products, suggesting that EI items should be supplemented by items measuring demographics and trip characteristics in future use.