In a wide range of engineering applications, complex constrained multi-objective optimization problems (CMOPs) present significant challenges, as the complexity of constraints often hampers algorithmic convergence and reduces population diversity. To address these challenges, we propose a novel algorithm named Constraint Intensity-Driven Evolutionary Multitasking (CIDEMT), which employs a two-stage, tri-task framework to dynamically integrates problem structure and knowledge transfer. In the first stage, three cooperative tasks are designed to explore the Constrained Pareto Front (CPF), the Unconstrained Pareto Front (UPF), and the $\epsilon$-relaxed constraint boundary, respectively. A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool. At the end of the first stage, each task selects strategies from this strategy pool based on the specific type of problem, thereby guiding the subsequent evolutionary process. In the second stage, while each task continues to evolve, a $\tau$-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks. enhancing the convergence and feasibility of the main task. Extensive experiments conducted on 32 benchmark problems from three test suites (LIRCMOP, DASCMOP, and DOC) demonstrate that CIDEMT achieves the best Inverted Generational Distance (IGD) values on 24 problems and the best Hypervolume values (HV) on 22 problems. Furthermore, CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs). These results confirm CIDEMT’s superiority in promoting convergence, diversity, and robustness in solving complex CMOPs.