To address the issues of fragmented entry points, lengthy operational paths, and inadequate adaptability of static interfaces for complex functions across diverse driving scenarios in intelligent cockpits, this paper proposes a resource allocation design method for automotive Human-Machine Interfaces (HMI) oriented toward complex functional task contexts. A closed-loop framework comprising “task context representation, user behavior modeling, functional relevance calculation, strategy scoring, and interface resource adjustment” is established. Firstly, a representation of complex functional task contexts is constructed by integrating multidimensional data, including vehicle status, task procedures, interaction history, spatiotemporal context, and user identity. Secondly, a user behavior model is developed by extracting long-term preferences from anonymized infotainment system telemetry data and incorporating short-term behavioral trends. Subsequently, the priority of candidate functions is calculated by synthesizing task-context relevance and user-behavior relevance. Finally, a collaborative adjustment scheme for the structural and content layers is generated, incorporating knowledge base constraints, strategy feasibility, and disruption cost considerations. The proposed method is validated through a controlled experiment on an infotainment system testbench, using seat comfort and spatial adjustment functions as the test case. The results demonstrate that, compared with conventional static designs, the proposed method significantly reduces function search time and task completion time, decreases the number of operational steps and erroneous inputs, and yields superior performance in terms of design efficiency, solution rationality, and user satisfaction. Consequently, it effectively enhances interaction efficiency and user experience in driving scenarios.