With the proliferation of social media platforms, especially commercial platforms today, and the ease of access to internet shopping, it is difficult to determine which products are good for clients based on reviews from users. Numerous successful innovations in the field of spam product review detection have been made. This study focuses on the creation of an efficient method for Vietnam spam product review detection using cutting-edge language models in addition to Vietnam-related work. For feature extraction, we utilize PhoBERT, a cutting-edge pretrained model for Vietnamese language processing. The downstream task model will be followed by the Graph Reinforcement Learning architecture, which has had many successes recently. Our proposed GRL model tries to capture text reviews into graph representations and utilize passing mechanisms for leveraging the product description as a global message passing to the review. By combining with the RL framework, we can construct the modulator, which controls the amount of information passing from the product description and gives out the best outcomes in the long term and outperformance compared to traditional methods.