The rapid evolution of video coding technologies has led to a substantial increase in encoding complexity, posing significant challenges for the practical deployment of encoders in various applications. Accurate control over encoding time is crucial for maintaining performance across diverse scenarios, but there are limited solutions available to address this requirement. This paper presents a novel neural network-based approach for frame-level complexity control within practical video encoders, facilitating the adaptive distribution of encoding resources and the selection of appropriate coding presets for individual frames. We have developed a modified version of the x265 encoder that supports dynamic frame-level preset adjustments. The proposed complexity allocation and feedback mechanism effectively regulate the encoding budget for each frame, ensuring optimal utilization of resources. Additionally, a lightweight neural network model is introduced to predict and select the most suitable coding preset based on the target encoding time. Extensive experiments conducted on Class B and UVG datasets have demonstrated the effectiveness of our scheme, with the average control error maintained at a remarkably low level of 1.9%. This research offers a promising solution for achieving precise control over encoding time, thereby enhancing the efficiency and adaptability of video encoders in real-world applications.