A reliability analysis method based on an improved dynamic Bayesian network was proposed to address the reliability issues of aircraft braking systems. Firstly, within the framework of dynamic Bayesian networks, combining dynamic logic gates with an improved conditional probability table modeling method, the failure rate of nodes was dynamically adjusted by introducing interference factors to determine the conditional probability table and marginal probability table of each node. Secondly, based on the dynamic operating characteristics of the aircraft braking system, a dynamic Bayesian network model of its normal braking system was constructed, and the reliability functions of each module component were derived based on the dynamic Bayesian network inference algorithm. Finally, the method was validated using simulated data generated by the Monte Carlo method. By comparing the reliability curve changes of each subsystem module before and after introducing interference factors, and combining the reverse inference ability of dynamic Bayesian networks, the posterior probability distribution of each module unit was analyzed to achieve system fault assessment. The experimental results show that the proposed method can effectively characterize the dynamic characteristics of aircraft brake systems, identify potential fault hazards, and provide theoretical support for the diagnosis and maintenance strategy formulation of normal brake systems.