General tasks on crowdsourcing platforms attract more and more workers with different skills and experiences. Existing approaches only leverage the information from tasks with feedback to evaluate worker ability. However, there are millions of tasks without feedback on the platforms. The participating behavior of workers involved in these tasks has not been exploited. In this work, we propose a worker ability model PB-Worker to support general tasks on crowdsourcing platforms. We model the worker latent relation and task latent relation by exploiting the worker participating behavior. To the best of our knowledge, this is the first work to consider the worker participating behavior. Our model is a semi-supervised model that can cover tasks with feedback and tasks without feedback. We employ the ladder network to generate the representations of workers and employ the neural network to predict the worker ability scores. A set of experiments against the real-world dataset from the Zhubajie platform has been conducted. Experimental results show that the output quality of the proposed approach is better than the existing baseline methods.