To address the issue of high computational complexity in coding units (CU) partitioning for versatile video coding (VVC) intra-frame coding, this paper proposes a CU fast partitioning algorithm based on DenseNet+FPN (feature pyramid network). The algorithm significantly reduces the encoding complexity of VVC, resulting in reduced encoding time. Firstly, a CU classification algorithm based on texture complexity is proposed to evaluate the texture complexity of CU blocks. Secondly, a network model based on DenseNet+FPN is introduced, utilizing multi-scale information to optimize CU partitioning to adapt to encoding requirements in various scales. Lastly, a novel adaptive quality-complexity balanced loss function is designed to balance encoding quality and computational complexity. Extensive experimental analysis is conducted for the proposed algorithm, and the results demonstrate that compared to VVC test model (VTM) 10.0, the average encoding time of the proposed algorithm is reduced by 44.268%, while the bjφntegaard delta bit rate (BDBR) only increases by 0.94%.