The association rules method is widely used in the analysis of retail trading data so as to guide the operational decision-making for category management, store layout and the commodity sales promotion of products. However, the data of the electronic commerce website is very huge, which leads to the low inefficiency of the associate rule. The fast clustering for sparse network of retail products associated big data is proposed. Firstly, the structure of the one step linked list is used to store the co-purchasing matrix. Then, the nodes with the low degree in the sparse network of retail products associated big data are pruned, which reduces the search space. Furthermore, the nodes of the sparse network of retail products associated big data are grouped by fuzzy k clustering with the idea that the nodes with high connectivity value are partitioned by the nodes with low connectivity value. Finally, the proposed algorithm is applied to analyze the trading data of the Amazon website, and the results show the effectiveness of the proposed method.