In order to solve the problem of high computing and storage costs of traditional algorithms and low accuracy of recommendation results, the research on cross-border e-commerce platform optimization and data-driven strategies in the ASEAN digital economy has been proposed. This paper first studies the information personalization algorithm of the artificial intelligence cross-border e-commerce shopping guide platform through big data technology, so that big data technology can be realized on the Hadoop platform. Then, it decomposes tasks into multiple tasks through Map, and uses Reduce to gather the decomposed multi task processing results together to obtain the final processing results. Finally, two MapReduce and one map are used to parallelize the user preference acquisition algorithm in the platform. According to user preferences, the products that match user preferences are obtained through association rule mining and recommended to users. The experimental results show that the proposed algorithm has the best effect among other algorithms when the click rate of recommended product information is 17.92%, indicating that the proposed algorithm recommendation results are more consistent with user needs.