Currently, most of searching methods for microblog use vector space model to calculate the relevance between the query and document. The statistical method of Term Frequency-Inverse Document Frequency (TF-IDF) is widely used to determine the weight of words. However, only using word as the unit of microblog searching is not enough to detect the whole information content of a microblog, which is usually the intent of the search users. To solve this problem, a learning to rank algorithm for microblogs based on analysis features and dynamic stepsize is proposed. Firstly, some analysis features for microblogs are defined, The features can be obtained through statistical analysis method, and used to predict user's behaviors. Secondly, a method to calculate the relevance of microblogs based on part of speech is proposed. It uses the strategy of information entropy to calculate POS information content of microblog and it can be used to predict the information content of the microblog. Finally, based on the existing ListNet algorithm, the concept of dynamic stepsize is introduced to optimize the calculation of stepsize, eventually a learning to rank algorithm for microblogs based on dynamic stepsize named Ranking based on Dynamic Learning Stepsize (RDLS) algorithm is formulated. The experimental results show that RDLS algorithm can get a more optimal training model by using either direct features or both direct and analysis features with the same iterations, and can attain better effect in microblog ranking compared with the ListNet algorithm.