Online matching with diversity and fairness pursuit, a common building block in the recommendation and advertising, can be modeled as constrained convex programming with high entropy. While most existing approaches are based on the single slot'' assumption (i.e., assigning one item per iteration), they cannot be directly applied to cases with multiple slots, e.g., stock-aware top-N recommendation and advertising at multiple places. Particularly, the gradient computation and resource allocation are both challenging under this setting due to the absence of a closed-form solution. To overcome these obstacles, we develop a novel algorithm named Online subGradient descent for Multi-slots Allocation (OG-MA). It uses an efficient pooling algorithm to compute closed-form of the gradient then performs a roulette swapping for allocation, yielding a sub-linear regret with linear cost per iteration. Extensive experiments on synthetic and industrial data sets demonstrate that OG-MA is a fast and promising method for multi-slots online matching.