This article takes consumer behavior in social network big data as the research object, systematically sorts out the relevant theories of social network big data, consumer behavior, and association rule mining, and constructs a consumer behavior association rule mining model adapted to social network scenarios. Firstly, preprocess social network consumer behavior data by collecting, cleaning, integrating, and reducing it; Secondly, optimize the FP Growth algorithm to address the drawbacks of traditional algorithms such as low efficiency and redundant rules when dealing with massive sparse data; Subsequently, complete frequent itemset mining, association rule generation, and filtering according to standardized procedures; Finally, application strategies are proposed in three major areas: precision marketing, product and service optimization, platform operation, and risk management, along with a three-dimensional support system of technology, data, and talent. Research has shown that the optimized FP Growth algorithm significantly improves the efficiency and accuracy of mining, and can effectively mine strong association rules such as consumer browsing interaction purchase, sharing add purchase, etc; The constructed mining model is adapted to the needs of social network scenarios, and the mining results can provide data support and practical references for the precise operation of enterprises and the sustainable development of social platforms. This article enriches the cross disciplinary achievements of social network big data and consumer behavior research, and improves the application system of association rule mining in non-transactional scenarios.

Wen Wen. Association Rule Mining and Application of Consumer Behavior in Social Network Big Data. International Journal of Frontiers in Sociology (2026), Vol. 8, Issue 1: 80-85. https://doi.org/10.25236/IJFS.2026.080110.

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