There are some processing problems of imbalanced data such as imbalanced data sets being difficult to integrate efficiently. This paper proposes and constructs a mixed penalty function data integration screening criterion, and proposes Differential Evolution Integration Algorithm Based on Mixed Penalty Function Screening Criteria (DE-MPFSC algorithm). In addition, the theoretical validity and the convergence of the DE-MPFSC algorithm are analyzed and proven by establishing the Markov sequence and Markov evolution process model of the DE-MPFSC algorithm. In this paper, the entanglement degree and enanglement degree error are introduced to analyze the DE-MPFSC algorithm. Finally, the effectiveness and stability of the DE-MPFSC algorithm are verified by UCI machine learning datasets. The test results show that the DE-MPFSC algorithm can effectively improve the effectiveness and application of imbalanced data classification and integration, improve the internal classification of imbalanced data and improve the efficiency of data integration.

The differential evolutionary ( $DE$ ) algorithm is a global optimization algorithm. To explore the convergence implied in the $Hilbert$ space with the parameter $\beta$ of the $DE$ algorithm and the quantum properties of the optimal point in the space, we establish a control convergent iterative form of a higher-order differential equation under the conditions of $P_{-\epsilon }$ and analyze the control convergent properties of its iterative sequence; analyze the three topological structures implied in $Hilbert$ space of the single-point topological structure, branch topological structure, and discrete topological structure; and establish and analyze the association between the $Heisenberg$ uncertainty quantum characteristics depending on quantum physics and its topological structure implied in the $\beta$ -Hilbert space of the $DE$ algorithm as follows: The speed resolution ${\Delta }_v^2$ of the iterative sequence convergent speed and the position resolution ${\Delta }_{x_{\beta }^{\epsilon }}$ of the global optimal point with the swinging range are a pair of conjugate variables of the quantum states in $\beta$ -Hilbert space about eigenvalues ${\lambda }_i\in \mathbb{R}$ , corresponding to the uncertainty characteristics on quantum states, and they cannot simultaneously achieve bidirectional efficiency between convergent speed and the best point precision with any procedural improvements. Where ${\lambda }_i\in \mathbb{R}$ is a constant in the $\beta$ -Hilbert space. Finally, the conclusion is verified by the quantum numerical simulation of high-dimensional data. We get the following important quantitative conclusions by numerical simulation: except for several dead points and invalid points, under the condition of spatial dimension, the number of the population, mutated operator, crossover operator, and selected operator are generally decreasing or increasing with a variance deviation rate $+0.50$ and the error of less than $\pm 0.5$ ; correspondingly, speed changing rate of the individual iterative points and position changing rate of global optimal point $\beta$ exhibit a inverse correlation in $\beta$ -Hilbert space in the statistical perspectives, which illustrates the association between the $Heisenberg$ uncertainty quantum characteristics and its topological structure implied in the $\beta$ -Hilbert space of the $DE$ algorithm.