Aiming at the problems of slow recognition and inability to achieve accurate prediction during the process of traditional BP neural network for breakout prediction

ACO algorithm was utilized to optimize the randomly selected weight threshold

and the optimization steps of ACO algorithm was introduced in detail. The neural network model was established based on the MATLAB software

and the optimized model was applied to breakout prediction. The historical data collected on the site was preprocessed

which was then input to the neural network model for training and testing. The result indicates that the recognition accuracy of ACO-BP breakout prediction model is significantly higher than that of traditional ones

where the forecast rate and reporting rate can reach 96.77% and 100%

respectively. The model not only speeds up the operation of the network model

but also ensures the global search ability and robustness of the model

which has desirable application prospects.