Using fixed rules to set travel recommendations and operational strategies only allows for simple matching operations based on limited and static information, resulting in low-quality tourism platform services. Therefore, this paper conducts strategic research on the construction and brand development of a red tourism cultural platform based on big data analysis. Regarding platform hardware design, this study selected high-performance servers and other equipment, optimizing configuration to enhance data processing capabilities and provide stable hardware support for the platform. Regarding software design, this study established a red tourism cultural resource library. After collecting information from multiple channels, cloud computing was utilized for integration, upload, and cloud-based processing, enabling centralized resource integration and intelligent management. Based on big data analysis, a preference prediction model was constructed by integrating behavioral indicators such as user click counts to accurately predict user preferences. Deeply mining historical user data to quantify interests and preferences, a hybrid similarity calculation method was used to screen matching content, enabling personalized recommendations and completing platform construction. Furthermore, a red tourism brand development strategy was developed, systematically planning implementation paths from the perspectives of resource integration and other dimensions to enhance brand competitiveness and influence. Test results show that this method stabilizes the characteristic distribution value of recommendation results within a high range despite changes in the number of users. The user click-through rate significantly increases and remains high as the amount of accessed data increases. The platform throughput increases from 125 request seconds with 50 concurrent users to 770 request/second with 600 concurrent users, effectively improving the platform’s service quality.
