Aiming at the problems of low mining efficiency, strong subjectivity and too many association relations generated by classical mining algorithms, a novel algorithm for association rule mining of high-dimensional data is designed in terms of both sample selection and association rule generation. The algorithm reduces the impact of weak samples at the beginning of mining by calculating the distribution coefficients and deletion thresholds of the samples and synthesizing custom support to double screen the samples at the first reading of the dataset. When generating frequent items and association rules, the algorithm mines information in a sample relationship table and sample the full relationship combination mode, which reduces the complexity and resource consumption of the mining process. The experimental results show that the number of frequent items and association rules mined by the Marc algorithm is significantly reduced, and the mining efficiency and memory consumption of the Marc algorithm are better than those of the Apriori, FP growth and Eclat algorithms. The higher the dimension is, the larger the data set is, and the more obvious the advantage is. The accuracy of the Marc algorithm for mining frequent items and association relations is 100%.