Modern video coding standards such as the audio video coding standard 3 （AVS3） have substantially improved compression efficiency， but conventional rate-distortion optimization still relies heavily on signal-fidelity measures such as mean squared error （MSE） and peak signal-to-noise ratio （PSNR）， which do not fully reflect the nonlinear characteristics of the human visual system （HVS）. Knowledge-distillation-based perceptual coding methods can improve visual quality by transferring bit-allocation knowledge from perceptually optimized learned image compression models to block-level quantization parameter （QP） maps. However， most existing approaches are designed and evaluated under image or all-intra coding configurations. When directly extended to practical video coding with both intra- and inter-frame prediction， a fixed perceptual QP adjustment strategy may produce content-dependent performance fluctuations， especially for sequences with complex textures or strong temporal variation. To address this problem， this paper proposes a lightweight content-adaptive QP decision method for AVS3 video coding. Before actual encoding， a video content pre-analysis module processes the first ten frames of each segment using a pretrained VGG-16 network. Features from shallow， intermediate， and deep layers are pooled and fused to describe fine-grained texture， local structure， and high-level semantics. Temporal variation of the fused features is further summarized into four compact descriptors： Texture， motion， complexity， and motion consistency. A logistic-regression classifier then generates a binary gating signal to determine whether the downstream perceptual optimization module is enabled or bypassed. The classifier is trained on 292 non-overlapping 32-frame segments constructed from standard HEVC test sequences， where the ground-truth label of each segment is determined by the MS-SSIM BD-rate difference between perceptual optimization and the AVS3 anchor under identical coding configurations. Five-fold stratified cross-validation yields an accuracy of 92.3%， a precision of 82.0%， a recall of 93.3%， and an F ₁ score of 87.3%. Experiments on representative 4K UHD sequences under random-access configuration show that， relative to the AVS3 HPM 12.0 anchor， the proposed method reduces the average BD-rate by 7.11% under multi-scale structural similarity （MS-SSIM） and by 3.39% under PSNR. When integrated into a highly optimized content-aware code （CAC）， the average MS-SSIM BD-rate relative to the common HPM anchor improves from -21.45% to -26.24%， corresponding to an additional gain of 4.79 percentage points， while the average PSNR BD-rate improves from -3.60% to -7.01%. On a synthetic 64-frame sequence containing abrupt scene changes， the proposed adaptive scheme achieves an 11.82% MS-SSIM BD-rate reduction and a -0.53% PSNR BD-rate， whereas unconditional perceptual distillation yields a 6.79% MS-SSIM reduction but a 1.71% PSNR increase. These results indicate that the proposed segment-level gating mechanism can retain perceptual coding gains while reducing the risk of objective-quality degradation caused by unconditional perceptual adjustment. The method does not alter the standard bitstream structure and can be integrated as a low-overhead encoder-side decision module in conventional hybrid video coding frameworks.