This paper proposes a genetic programming (GP) algorithm for symbolic regression (SR), called the behavioral cloning guided genetic programming (BCGP) algorithm. The goal is to improve the effectiveness of crossover by preserving the relationship between parent operators and subtrees through imitating the behavior of subtree crossover during evolution. Specifically, BCGP investigates the application of a multilayer perceptron to capture features based on the parent operators and the subtrees. Across the benchmark problems from the SR benchmark and the Feyman SR database, BCGP gains the lowest count of maximum average MAEs and lowest average ranks, and statistically significantly outperforms ellynGP, and GP-GOMEA on 31 and 9 benchmark problems, respectively, out of a total of 43.