In order to improve the utilization rate of brain imaging big data and solve the fusion problem of multi-source and heterogeneous brain imaging big data, an improved brain imaging big data ant colony optimization algorithm (BigDataACO) is proposed to complete the multi-source brain imaging big data information in the feature layer and decision-making and the problem of multi-source data fusion was solved. The swarm intelligence algorithm is a process of simulating the complex problem of populations in nature through the mutual cooperation between individuals. The algorithm has potential parallelism and strong robustness, and the algorithm does not depend on specific problems. The definition, principle and implementation method of brain imaging big data fusion problem are studied. Then the insufficiency of big data fusion modeling algorithm is analyzed. Finally, the source and core steps of ant colony big data fusion algorithm are studied. The experimental results show that the improved BigDataACO algorithm is verified by the measured data. Compared with K-means, D-S evidence theory and Bayesian algorithm, the uncertainty of data fusion is greatly reduced by the improved algorithm proposed in this paper.