Kernel feature ranking often delivers many benefits for big data mining, e.g., improving generalization performance. However, its efficiency is quite challenging due to a need of tuning kernel parameters in the ranking process. In present work, we propose a computational-light metric based on kernel class separability for kernel feature ranking. In the proposed metric, the kernel parameter is optimized by a proposed analytical algorithm rather than an optimization search algorithm. Experimental results demonstrate that (1) the proposed metric can lead to a fast and robust kernel feature ranking; and (2) the proposed analytical algorithm can select a right kernel parameter with much less computation time for two state-of-the-arts kernel metrics.