<p>Fuzzy quadratic programming arises in many optimization problems and has important applications in science and engineering. This study employs core properties of projection operators to solve fuzzy quadratic optimization problems through a projection recurrent neural network approach and a convex energy function. Due to the limited research on solving fuzzy quadratic programs through neural networks, we introduce a simplified projection model for this purpose. The original problem is first reformulated into an interval problem and subsequently into a weighting problem, achieving the Karush–Kuhn–Tucker optimality conditions. These conditions are then embedded into a neural network architecture as the primary tool for solving the problem. Additionally, we explore the global convergence and Lyapunov stability properties of the system. Finally, we present various simulation instances to validate the results obtained. Two practical examples, the fuzzy shortest path problem and the fuzzy matrix game, are also included.</p>