Distant supervised relation extraction has been widely used to identify new relation facts from free text. However, relying on a single-node categorization model to identify relation facts for thousands of relations simultaneously inevitably accompanies with serious false categorization problem. Also to the best of our knowledge, no previous efforts has yet considered to update the categorization model with the new identified relation facts, which wastes the chance to further improsve the extraction precision and recall. In this paper, we novelly propose a multi-level distant supervision model for relation extraction, which divides the original categorization task into a number of sub-tasks in multiple levels of a constructed tree-like categorization structure. With the tree-like structure, an unlabelled relation instance would be categorized step by step along a path from the root node to a leaf node. Beyond that, we propose to do bootstrapped distant supervision to update the distant supervision model with new learned relation facts iteratively to further improve the extraction precision and recall. Experimental results conducted on two real datasets prove that our approach outperforms state-of-the-art approaches by reaching more than 10% better extraction quality.