Advances in high spatiotemporal resolution millimeter-wave radar technology have significantly improved range and velocity resolution, thereby causing targets to span multiple adjacent cells in the range-Doppler map and form extended structures. These structures violate the point-target assumption underlying conventional constant false alarm rate algorithms, leading to inaccurate background noise estimation and consequently, incomplete detection of extended targets. To address this issue, this paper proposes a millimeter-wave radar detection method based on convolution and entropy-guided weighted sampling. The method first performs region enhancement on the range-Doppler map using a cross-shaped convolutional kernel, which reinforces spatial continuity within target regions. It then estimates the global background noise using weighted random sampling guided by the entropy distribution across the range-Doppler map, which replaces the conventional sliding reference window structure and achieves more accurate background noise estimation. Simulation results demonstrate that the proposed method achieves 90% detection probability at an signal-to-noiseratio(SNR) of -5 dB, showing a notable improvement in extended target detection performance over existing methods. Field experiments further verify its effectiveness in preserving target structural integrity and enhancing point cloud density. In two representative traffic scenarios, the proposed method increases the number of detected points by 21.51% and 111.11%, and 39.38% and 136.87%, respectively, indicating its potential for robust millimeter-wave radar perception in complex environments.