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.