Dark current, as an inherent noise in photodetectors, significantly affects the detection performance of dual-beam spectrophotometers. To mitigate this impact, an adaptive filtering dual-beam spectrophotometer is proposed. Firstly, a real-time dark current detection system is designed to capture the sample signal, reference signal, and dark current signal within each detection cycle. Secondly, an adaptive cascaded filtering algorithm called Recursive Least Squares and Normalized Least Mean Squares (RLS-NLMS) is introduced to address the issue of dark current elimination caused by temporal differences. The algorithm employs a three-level cascaded filtering model. The first level, local mean (LM), reduces the correlation between dark current and the detection signal. The second level utilizes a low-order RLS filter for signal filtering, while the third level employs a high-order NLMS filter to process the output signal from RLS. Experimental results demonstrate that the adaptive filtering dual-beam spectrophotometer achieves a relative error of only 1.46% in transmittance ratio measurement, reducing it by 66% compared to traditional methods. It exhibits excellent performance in dark current cancellation and holds significant practical value.