The construction of a characteristic town is an important measure to promote the economic transformation and upgrading of China. It also promotes the coordinated and balanced development between urban and rural areas, and the construction of new urbanization and new rural areas. At present, China’s township governments are actively declaring construction projects of characteristic town. Considering the limitation of resources and funds, the government needs to choose projects with better foundation and better development prospects to invest and build, to avoid the waste of resources brought about by blind construction. Starting from the pre-selection of the construction projects of the characteristic town, taking Liaoning Province as an example, this research divided the characteristic town into four categories. Initially established the characteristic town evaluation index system blending comprehensive and multivariate index; then optimizes the index system through the statistical analysis, reliability analysis; and validity analysis of the questionnaire survey to finally obtain the selection index system of the construction projects of the characteristic town of Liaoning Province. Secondly, using the combination of Analytic Network Process (ANP) and Improved Technique for Order Preference by Similarity to an Ideal Solution (Improved-TOPSIS) method, the selection evaluation model of tourism-type characteristic town construction projects are obtained. Finally, taking the three tourism-type characteristic town projects declared by Liaoning Province as an example, using the ANP and Improved-TOPSIS evaluation model to evaluate and compare the three towns. The priority of “Xietun Town, Tanghe Town, Zhaoquanhe Town” is obtained. The result is consistent with the recommended order and verifies the applicability of the selection model. At the same time, through the feedback of the evaluation process, the current development constraints of the three towns are clarified, and the future development direction of the town is pointed out.
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