The seagull optimization algorithm (SOA) is a novel metaheuristic algorithm based on group behavior. However, since SOA suffers from slow convergence, insufficient population diversity, and easily falls into local optimality, a multi-mechanism seagull optimization algorithm (MSOA) that incorporates nonlinear convergence factors, nonlinear weights, evolutionary boundary constraint processing, and Gaussian mutation is proposed as a result. Meanwhile, to verify the signature of the algorithm's optimization-seeking effect, six excellent metaheuristic algorithms are selected for comparison based on 14 different features of the test functions. The experimental results show that MSOA not only has a higher optimization-seeking accuracy but also has a faster convergence speed and stronger robustness. In addition, a BP neural network classification model based on MSOA was designed using the iris dataset as the data source, and according to the experimental statistics, the BP neural network classification model improved by MSOA has practicality and effectiveness.