A mixture of experts parallel recommendation algorithm framework which combined feature fusion and distribution was proposed in order to address the issues of parameter sharing and high computational costs in click-through rate prediction. The ability of parallel architecture can be improved to distinguish different types of features and learn more expressive feature inputs, and parameters between explicit and implicit features can be shared. The gradients during backpropagation were mitigated and the performance of the model was improved. The framework is lightweight and model-agnostic, and can be generalized to a variety of mainstream parallel recommendation algorithms. Extensive experimental results on three public datasets demonstrate that the algorithm framework can be used to effectively improve the performance of SOTA models.