Aiming at the problem of quick process parameters set for new product with complex process which responded to the necessity of customer in process manufacturing environment, a data-driven matching method for processing parameters was proposed as process knowledge service. The features extracted from different products were acquired in manufacturing data by information theory, and the relationship model of product standards and process parameters based on ensemble-classification was constructed to realize the process similarity analysis. Through comparing the similarity of historical production data and target product's manufacturing requirement, the near-optimal process parameters were selected. The proposed method was applied to the hot-rolling process of steel plate, and the result showed the effectiveness on providing process knowledge service and conformity for dealing with coupling knowledge discovery.