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Secure multi-party computing (MPC) allows joint computing without disclosing the private data of each participant. However, the existing computing tasks often involve the analysis and processing of multi-party massive data sets, which significantly reduces the actual availability of MPC. Improving the volume of MPC data processing is one of the main research directions at present. In order to improve the ability of MPC to process large-scale data, the MPC algorithm is combined with the data parallel analysis framework. Based on the idea of minimizing multi-party computing tasks, a secure multi-party computing efficiency optimization technology is proposed. The directed acyclic graph of the algorithm is created, the MPC nodes and non MPC nodes are marked, and the techniques of static analysis, query rewriting transformation and partition heuristic are used to minimize the amount of MPC calculation and improve the concurrency of calculation. Taking multi-party linear regression as an example, this paper discusses the secure multi-party computing technology suitable for big data analysis. The experimental results show that the proposed secure multi-party computing optimization technology can significantly reduce the computing time under the condition of ensuring the computing accuracy. The algorithm improves the efficiency of the system and enhances the practical ability of MPC.