Based on the big data algorithm, this paper provides a detailed description of the SVM model, optimizes the algorithmic process of SVM with the GWO algorithm, and constructs the GWO-SVM classification model. The GWO-SVM is used for data mining analysis of image perception of tourism scenic planning and development, and three factors affecting image perception, namely image perception attributes, emotional image, and exploratory variables, are mined with the example of a city tourism scenic area. Regarding image perception attributes, the average percentages of functionality, wholeness, psychology, and uniqueness are 79%, 81.97%, 59.35%, and 17.95%, respectively. The perfect tourism facility function and good tourism atmosphere are the tools to enhance tourists’ image perception of tourist attractions. In terms of emotional image data, the percentage of “excited” and “pleasant” emotions are 25.66% and 27.06%, respectively, while the percentage of “frustrated” emotions is only 2.93%. In the exploratory variables, the percentage of “excited” and “happy” emotions were 25.66% and 27.06%, respectively, while the percentage of “frustrated” emotions was only 2.93%. Among the exploratory variables, the approval rates of tourism facilities, natural and humanistic landscapes, and scenic activities are 65.09%, 48.29%, and 47.52%, respectively, which means that play facilities, various landscapes, and preferential activities are good ways to enhance the image recognition, of scenic spots. The reasonable use of big data analysis technology can make the goal of planning and developing tourist attractions clearer and also help to improve the image recognition of tourist attractions and increase the economic benefits of tourist attractions.