Versatile Video Coding (VVC) achieves impressive coding gain improvement (about 40%+) over the preceding High-Efficiency Video Coding (HEVC) technology at the cost of extremely high computational complexity. Such an extremely high complexity increase is a great challenge for power-constrained applications, such as Internet of video things. In the case of intra coding, VVC utilizes the brute-force recursive search for both the partition structure of the coding unit (CU), which is based on the quadtree with nested multi-type tree (QTMT), and 67 intra prediction modes, compared to 35 in HEVC. As a result, we offer optimization strategies for CU partition decision and intra coding modes to lessen the computational overhead. Regarding the high complexity of the CU partition process, first, CUs are categorized as simple, fuzzy, and complex based on their texture characteristics. Then, we train two random forest classifiers to speed up the RDO-based brute-force recursive search process. One of the classifiers directly predicts the optimal partition modes for simple and complex CUs, while another classifier determines the early termination of the partition process for fuzzy CUs. Meanwhile, to reduce the complexity of intra mode prediction, a fast hierarchical intra mode search method is designed based on the texture features of CUs, including texture complexity, texture direction, and texture context information. Extensive experimental findings demonstrate that the proposed approach reduces complexity by up to 77% compared to the latest VVC reference software (VTM-23.1). Additionally, an average coding time saving of 70% is achieved with only a 1.65% increase in BDBR. Furthermore, when compared to state-of-the-art methods, the proposed method also achieves the largest time saving with comparable BDBR loss. These findings indicate that our method is superior to other up-to-date methods in terms of lowering VVC intra coding complexity, which provides an elective solution for power-constrained applications.