In order to improve the quality of pseudo-labels and solve the problem of confirmation bias in Semi-Supervised Object Detection （SSOD）， an SSOD framework based on dynamic parameters under guidance of Self-Paced Learning （SPL） was proposed. In the framework， dynamic self-paced parameter and continuous weight variable were designed to optimize the effect of SSOD. In specific， the dynamic self-paced parameter was used to evaluate difficulty of the samples based on real-time performance of the model during training process， the continuous weight variable was used to evaluate importance and reliability of each sample in training accurately by comparing relationship between sample loss and dynamic self-paced parameters， and refine weight design of each object in the samples. In addition， a single model was used in the framework for iterative training， and a consistency regularization strategy was introduced to evaluate consistency of the model predictions. This design provided more targeted weight information for the model， and optimized the training process adaptively by the model through dynamic adjustment of the weight information. Extensive comparison experimental results on PASCAL VOC and MS-COCO datasets show that the proposed framework improves the detection accuracy of the model significantly， and verify good generality and efficient convergence performance of the framework. Especially on PASCAL VOC dataset， the proposed framework has the detection precision improved by 0.65， 4.84， and 0.28 percentage points， respectively， compared with LabelMatch， Unbiased Teacher V2， and MixTeacher.