In relative gradient algorithms of Independent Component Analysis(ICA), careful selection of step size is important to obtain good performance. In this study, an adjustable rate of relative gradient algorithm was proposed. With the changes of iteration number, the learning rate of relative gradient algorithm changed correspondingly，which solved the problem about the contradiction between the convergence rate and stability well. On this basis，this method was adopted in Blind Signal Separation(BSS) problems，and its effectiveness was validated by simulation. Adjustable rate algorithm with relative gradient has good prospects in independent component analysis.