A dual-stream framework named Patchflow was proposed to address the limited ability to capture multi-scale dynamic and insufficient nonlinear modeling capability in long-term time series forecasting. A multi-scale segmentation strategy was employed to partition long-term time series into local segments of different granularities, enabling accurate simultaneous modeling of local and global temporal dynamics while enhancing adaptability to multi-frequency patterns. A Chebyshev polynomial-based approximate convolution operator was further introduced to capture inter-segment correlations from a geometric perspective, improving the modeling accuracy of non-stationary sequences. Based on the trend modeling branch, a structured prior was introduced to extract complex temporal dependencies. Experiments on multiple public datasets showed that Patchflow consistently outperformed state-of-the-art methods, with particularly notable improvements on sequences with strong seasonality and long-term trends.