Abstract: The difference of the quantitative transaction data from the common transaction data is that the value of each dimension is quantitative, not binary. The study of the efficient method for similarity search on the quantitative transaction data is very important and challenging. A new function Hsim（） is presented to measure the proximity of objects in high dimensional spaces. The function can overcome the shortcoming of L p-norm and other distance functions, and adapt to binary and numerical data. According to the characteristic of the quantitative transaction data, a similarity indexing structure based on Hsim（） is constructed, and an algorithm for similarity search on quantitative transaction data is also described. Experiments demonstrate that this method has very good pruning efficiency for similarity search on the quantitative transaction data, so it can greatly speed the similarity search.