In recent years， significant progress has been made in applying Generative Adversarial Network （GAN） in Low-Dose Computed Tomography （LDCT） image denoising. However， the existing methods face challenges such as insufficient modeling capability for complex noise distribution and limited ability to preserve structural details. Therefore， a multi-path GAN for LDCT image denoising — Trident GAN was proposed. Firstly， a feature guided generator Trident Uformer was designed. In this generator， a Feature Polymerization Attention （FPA） module was added at the bottleneck layer of the U-Net structure， thereby solving the problem of low spatial resolution in a U-shaped structure. Secondly， a multi-path feature extraction submodule Trident Block was designed， and in each of the three blocks， a Local Detail Enhancement Block （LDEB） was introduced to extract detailed features， a Lightweight Channel Attention Block （LCAB） was incorporated to enhance channel features， and a Spatial Interaction Attention Block （SIAB） was utilized to capture important spatial features， respectively. Within the SIAB， a multi-level interactive attention function and evaluation mechanism were employed to design a Spatial Context Attention Mechanism （SCAM）， which addresses the limitations of single-attention mechanisms. Finally， a Multi-Feature Fusion （MFF） module was designed to realize feature aggregation at the end of the three blocks， and model both local detail information and global semantic information， and solve the problem of discontinuous details across different levels. Furthermore， the Multi-Scale Pyramid Discriminator （MSPD） was used to check the quality of the generated results at different dimensions， and guide the generation of globally consistent images. Experimental results show that Trident GAN achieves the average Peak Signal-to-Noise Ratio （PSNR） and Structural Similarity Index Measure （SSIM） of 31.519 3 dB/0.883 0 and 33.633 1 dB/0.947 8， respectively， on Mayo and Piglet datasets. Compared with High-Frequency Sensitive GAN （HFSGAN）， this method has the number of parameters reduced by 75.58% and the test time reduced by 36.36%. It can be seen that compared with the existing methods such as HFSGAN， Trident GAN improves image quality with less computational load.