As a novel schema of software development, software crowdsourcing has been widely studied by academia and industry. Compared with traditional software development, software crowdsourcing makes the most use of developers all over the world to complete complex development tasks which can effectively reduce costs and improve efficiency. Nevertheless, because there are a large number of complex tasks in the current crowdsourcing platform and inaccurate task matching will affect the progress and quality of task solutions, it is very important to study the matching problem between developers and tasks. Therefore, this study utilizes the dynamic preferences and competitiveness features of developers and proposes a task recommendation model to recommend appropriate software development tasks for developers. First, the attention mechanism based-long short-term memory network is adopted to predict the current preference of a developer to screen out the top-N tasks that conform to the preference from the candidate tasks. On this basis, according to the developer’s competitiveness, differential evolution algorithm based-extreme gradient boosting is used to predict the developer’s scores of top-N tasks, thus further filtering out the top-K tasks with the highest scores to recommend to the developer. Finally, in order to verify the validity of the proposed model, a series of experiments is carried out to compare the existing methods. The experiment results illustrate that the proposed model has significant advantages in task recommendation in software crowdsourcing.