In this paper, an improved elitist clonal selection algorithm (ECSA) is proposed to realize the automatic knot adjustment of the Bspline curve approximation. In order to improve the search efficiency and solution quality of the algorithm, an adaptive chaotic mutation operator is designed, and an antibody reselection strategy based on the antibody concentration and antigen affinity vectorial moment is proposed. Then Bayesian Information Criterion (BIC) is used as the affinity metric to weigh and judge the goodness of fitting and computational complexity. Further, the improved algorithm achieves a balance between depth search and breadth optimization, and can automatically and accurately calculate the number and locations of internal knots, thus the Bspline curve approximation of the data points is completed. Simulation and experiment results show that the proposed algorithm not only can efficiently and accurately realize the automatic Bspline curve approximation of the noisy complex data with the features of continuity, discontinuity and cusps, but also possesses better global convergence and convergence speed compared with current researches.