The invention discloses a Handle recognition system analysis load balancing method based on a neural network. The method comprises the steps of establishing an enterprise-server mapping table, recording time sequence data, training and generating a load utilization rate prediction model and task load prediction, and updating the enterprise-server mapping table according to a prediction result. According to the invention, firstly, the enterprise-server mapping table is established to accelerate the task response speed and improve the task processing efficiency, the time sequence data and the BP neural network are used to generate the load utilization rate prediction model and improve the prediction accuracy, then the task quantity is predicted through the Elman neural network. the task quantity is input into a load utilization rate prediction model to estimate the load change of each server, and finally, a load utilization rate piecewise function and a strategy of dynamically modifying a mapping table are combined, so that the server cluster can well cope with the Handle recognition system analysis task, the utilization rate and the load balance degree of the server cluster are improved, and the time for executing the task is shortened.