Early Warning and Management Method of Abnormal Performance of Tourist Scenic Spots Assisted by Image Recognition Technology

Abstract

Scenic area is a product of the improvement of living standards and the improvement of economic level. Many types of scenic spots have been developed in most areas. The development of scenic spots will affect the economic development level of a region, and it will also affect the living standards of local residents because the construction of scenic spots will consume a lot of financial and human resources. If the scenic area can be managed well, it will bring greater economic benefits to the local area. However, if the scenic area fails to operate, it can affect the local finances. This requires local managers to be able to grasp the development of the scenic area, which will avoid abnormal performance. However, the performance management of scenic spots is more difficult for local managers, and more cumbersome data will be involved here. This study uses the convolutional neural network (CNN) method to realize the image recognition technology of the characteristics of the scenic area’s flow of people and tourists’ preferences, and these characteristics will be displayed to the managers in the form of images. In this study, the collaborative filtering algorithm can be used to complete the active recommendation of abnormal performance of tourist scenic spots. This also enables CNN to achieve collaborative monitoring. The research results show that image recognition technology can better assist managers to manage the abnormal performance of scenic spots. CNN also has good accuracy in predicting related features such as the flow of people in scenic spots. The similarity index of the three features exceeds 0.9. This has achieved a high accuracy for anomaly detection of tourist attractions. The largest similarity index has reached 0.963.