The Multi Agent Information Fusion (MAIF) system can alleviate the limitations of a single expert system when dealing with complex situations, as it allows multi-agent collaboration to solve problems in complex environments. The D-S evidence theory has important applications in fields such as multi-source data fusion and pattern recognition. However, when dealing with highly conflicting data, traditional Dempster combination rules may produce counterintuitive results. Therefore, we propose a conflict data fusion method for multi-agent systems based on reconstructing basic probability assignment and belief entropy. Firstly, use the reconstructed basic probability assignment (BPA) to construct the initial trust level for each agent. Then, by calculating belief entropy, the information content of each evidence group is obtained, thereby correcting reliability and obtaining more reasonable evidence. Finally, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples demonstrate the effectiveness of this method and improve the accuracy of the MAIF system identification process.