## 1 Introduction  
In data classification, the most common methods used are clustering methods based on statistical theory [1] such as naive Bayes [2] and artificial neural networks (ANNs) [3]. These methods can use large-scale imbalanced data as clustering targets [4]. However, with the development of large data networks, electronic data integration, imbalanced data analysis, large text databases, quantum data decoding and encoding analysis, and other data types in recent years, the efficient processing of data has become the foremost subject of current data classification and processing. The imbalanced data classification system studied in this paper belongs to this subject. When the imbalanced data is classified and processed, integration and classification of the data has gradually become the mainstream method for processing imbalanced data but this method usually results in a higher computational cost [5]. In view of the cause, we have identified a meaningful conclusion--the efficient clustering of imbalanced data processing tends to increase the processing cost. For the processing of imbalanced data, there are some mature algorithms such as sampling methods [6, 7], cost-sensitive algorithms [8, 9] and one-class classification [8, 9]. However, there are some problems in that the processing of imbalanced data is mainly confined to the level of computer algorithm analysis and big data analysis, and lacks the function of efficient integration, which can result in privacy or data breaches, resulting in significant losses to data owners. Distributed and compatible construction of evolutionary algorithm structures is an innovative idea, which was proposed by [PERSON] [10]. Centroid classification algorithm will provide new ideas for us to explore and solve imbalanced data classification problems. [PERSON] proposed a new idea about data processing. Application level and resource level of big data can improve investment efficiency and business performance, which idea was proposed by [PERSON] and it is applied preferentially to business [11]. In addition to the above algorithms, there are other scholars who have performed research on imbalanced data processing. To seek parameter optimization to reduce the cost before training the model, [PERSON] proposed a fault-tolerant recognition algorithm that sets the artificial cost rate of data processing to ultrahigh parameters [12]. In the integration of imbalanced data, most experts focus on how to improve the efficient integration and the integration of imbalanced data, such as the boosting data analysis method [13] mentioned by [PERSON] and the bagging data integration analysis algorithm [14] proposed by [PERSON]. [PERSON] proposed a modular data analysis integration strategy that classifies and integrates multiple imbalanced data and analyzed data using modular strategies [15]. [PERSON] proposed a data integration strategy combining the (Synthetic Minority Oversampling Technique) SMOTE algorithm and boosting algorithm used in the sampling process [16]. [PERSON] [17] proposed a new effective recursive algorithm combining \(1 BC\) and \(2 BC\) Bayesian structural systems, improving the difference between computer systems and artificial data in low-dimensional levels. For generalized imbalanced data processing, the introduction of the above algorithms is limited to the specific algorithm program of data integration and the results of data classification. However, for the number of feature samples and the feature categories about the imbalanced data, the above algorithms may cause higher data classification costs and classification errors. Overall, in the previous research methods, most of them are integrated strategies under a single integrated classifier structure model such as support vector machine, decision tree analysis and the naive Bayesian method [18]. These methods determine the integration effect based on data performance as a classification criterion, which is not conducive to the continuous integration and classification of imbalanced datasets.