An adaptive feature fusion cascaded Transformer retinal vessel segmentation algorithm is proposed in this paper to address issues such as pathological artifacts interference, incomplete segmentation of small vessels, and low contrast between vascular foreground and non-vascular background. Firstly, image preprocessing is performed through contrast-limited histogram equalization and Gamma correction to enhance vascular texture features. Secondly, an adaptive enhancing attention module is designed in the encoding part to reduce computational redundancy while eliminating noise in retinal background images. Furthermore, a cascaded ensemble Transformer module is introduced at the bottom of the encoding-decoding structure to establish dependencies between long and short-distance vascular features. Lastly, a gate-controlled feature fusion module is introduced in the decoding part to achieve semantic fusion between encoding and decoding, enhancing the smoothness of retinal vessel segmentation. Validation on public datasets DRIVE, CHASE_DB1, and STARE yielded accuracy rates of 97.09%, 97.60%, and 97.57%, sensitivity rates of 80.38%, 81.05%, and 80.32%, and specificity rates of 98.69%, 98.71%, and 98.99%, respectively. Experimental results indicate that the overall performance of this algorithm surpasses that of most existing state-of-the-art methods and holds potential value in the diagnosis of clinical ophthalmic diseases.