The knee joint is a complex biotribological system. Its health relies on the interaction between articular cartilage and synovial fluid. Specifically, cartilage acts as a poroelastic structure, while synovial fluid exhibits complex non-Newtonian behavior. To elucidate the lubrication mechanisms, it is essential to investigate the fluid-structure coupling between them. This study presents a computational framework using Physics-Informed Neural Networks (PINNs) to solve the fractional squeeze-film flow problem in a knee joint model. The model characterizes the synovial fluid as a fractional Maxwell fluid, capturing its viscoelastic memory effects. At the same time, the cartilage is a porous elastic layer, and the femoral component is a rigid sphere. By directly embedding the governing equations into the neural network's loss function, this complex flow behavior can be effectively solved. The results indicate that the permeability of the cartilage layer is a critical parameter regulating joint lubrication performance. High permeability accelerates the exudation of synovial fluid from the loaded region, leading to a rapid decline in film thickness and a significant reduction in fluid load-supporting capacity. Meanwhile, an increase in the fractional order and a decrease in the relaxation time also weaken the stability and load-bearing performance of the fluid film, further accelerating lubrication failure. Collectively, these mechanisms exacerbate cartilage wear and elevate the risk of osteoarthritis. This study provides novel insights into the lubrication failure of diseased cartilage. It also highlights the potential of PINNs for tackling complex biomechanical problems.