Aiming at the computational complexity of least squares support vector machine(LSSVM)’s online modeling, an online learning algorithm for LSSVM was proposed. First, the solution of LSSVM through the Cholesky factorization was introduced, then the Cholesky factorization of partitioned matrix was applied to the online solution of LSSVM according to the updating character of kernel function matrix during online modelling, and triangle factor matrix was renewed online, consequently, a novel online learning algorithm for LSSVM was obtained. The improved learning algorithm can make full use of the historical training results and reduce the computation amount. The numerical simulation results the validity of the online learning algorithm for LSSVM.