Abstract：It is difficult to determine the penalty parameter and kernel function parameter of least square support vector regression(LSSVR). Additionally, artificial bee colony(ABC) is easy to fall into local optimum and its convergence speed is slow. So, we propose an improved artificial bee colony(IABC) to optimize the parameters of LSSVR and do research on deformation prediction. First, IABC generates positive and negative populations to increase the diversity of the initial group using the reverse learning strategy. After one iteration, information is exchanged between the optimal food sources of two populations to achieve optimal selection. Furthermore, we design an adaptive weight function and adaptive selection function to balance the exploration and development capacity of ABC. Second, we consider the predictive accuracy of LSSVR as the objective function, and transform it into the fitness function of IABC, thereby building a prediction model based on IABC optimization LSSVR. Then, taking the monitoring data of foundation pit as an example, we compare the prediction effect of the LSSVR model optimized by IABC, the LSSVR model optimized by ABC, and the combination model based on PSO. The results show that IABC increases the diversity of the population and improves the convergence accuracy. The prediction trend based on the IABC optimized LSSVR model is more practical and the prediction accuracy is higher than the contrast model.