Big Data Classification Model and Algorithm Based on Double Quantum Particle Swarm Optimization

For the traditional big data classification models and algorithms that have the drawback of long processing time, the use of intelligent algorithms to model identification of multivariable systems has become a mainstream research direction. Discussion This paper proposes an efficient solution for data-parallel optimized computing. The problems in the identification of multi-variable systems that integrate historical big data and intelligent algorithms are analyzed, and the impact of input variables on output is quantified. Build a big data classification model, mine valid data points, arrange data combination methods, divide data local connection methods, obtain local node micro-cluster data, calculate redundant data and invalid data increment values in node data groups, and reconstruct central node sample algorithm, adjust the integrated data classification strategy, optimize and update the data integration classification method. Design the experiment, simulate the experimental environment, and verify that the proposed model calculation method can shorten the data processing time in practical application, which has practical research value. Through the design of the overall process of the algorithm, data classification to form a data set, and then describe the steps of the algorithm, and finally verify the feasibility of the algorithm through simulation experiments, which can not only greatly reduce the communication cost between network nodes, but also greatly improve the global mining accuracy and improve the query efficiency in distributed data, It has good utilization value in the analysis of massive data information.