This paper uses Brainstorm Optimization Algorithm (BSOA) to analyze network data. First, collect the network data of the battery factory production line/core network N6 port, and then evaluate the clustering effect under different iteration times and grouping effects through three evaluation methods: SC, CHI, and DBI. The results show that BSOA clustering divides the network data into eight groups, and the effect of iteration 6 is the best. This result is consistent with the application scenario of the plant; in addition, the effect is very different when comparing iterations 6 and 7, and it is inferred that it is caused by overfitting.