Abstract: Sequential recommendation is centered on mining users' preferences and behavior patterns from their interaction sequences. Existing works have recognized the inadequacy of single-modal interaction data, and have utilized a large amount of multimodal data, including item reviews, homepage images, and other sources, to complement interaction data and improve recommendation performance. However, these multimodal data are often interspersed with unavoidable noise that may limit the exploration of personalized user preferences. While suppressing inter-modal inconsistent information can reduce noise interference, it is almost impossible to completely eliminate noise from user-generated multimodal content. To address the above challenges, we propose a Large language model-based Trusted multi-modal Recommendation (Large-TR) algorithm, which aims to provide trustworthy recommendation in noisy multi-modal data scenarios. Specifically, the algorithm relies on the excellent natural language understanding capability of the large language model, which efficiently filters the noise in multimodal data and achieves more accurate and detailed modelling of user preferences. Additionally, we design a trustworthy decision mechanism that dynamically evaluates the uncertainty of recommendation results and ensures the usability of recommendation results in high-risk scenarios. Experimental results on four widely used public datasets show that the algorithm proposed in this paper has better performance compared to other baseline algorithms. Our source code is available at https://github.com/ hhbray/Large-TR.