Research on Optimization Strategy of English Teaching Resource Allocation Based on Intelligent Data Analysis

Considering the English teaching resource allocation problem as a combinatorial optimization problem with multiple constraints, this paper designs an intelligent grouping strategy based on particle swarm genetic algorithm applied to English teaching resource allocation. First, a new parameter matrix is added to the standard Bayesian knowledge tracking model to construct the CS-BKT model, which tracks the English level of students. Genetic operations are incorporated into the particle swarm algorithm, which is updated using crossover operations and its own mutation operations. Among them, the object of crossover operation performed by the particles comes from each iteration, and the individual extreme value and the population extreme value derived from comparing the fitness value according to the objective function, which improves the convergence efficiency of the population. Experiments are designed to verify the effectiveness of this paper’s algorithm, and it is found that the grouping accuracy of this paper’s grouping algorithm reaches up to 98%, which is much higher than that of the baseline strategy, which is below 80%. The grouping time is within 2s, which is also lower than the two baseline models, and it can efficiently allocate English teaching resources. This paper innovates an optimization path for allocating English teaching resources.