Memristors have emerged as a key building block for artificial neural networks (ANNs), offering energy efficiency and high scalability for hardware-based synaptic weight updates. As device miniaturization is crucial for enhancing memristor performance, hexagonal boron nitride (h-BN) stands out as a promising resistive switching medium due to its excellent insulating characteristics even at an atomically thin scale. However, conventional h-BN memristors suffer from abrupt switching behavior by uncontrollable filament formation, limiting their potential for ANN applications. Here, h-BN-based memristors exhibiting linear and symmetric analog switching by leveraging multiple nano-filament confinement is presented. The geometric confinement between suspended h-BN films and the apexes of GaN nano-cones facilitates analog switching behavior, reducing cycle-to-cycle variation and ensuring stable consecutive operations. Electrical analyses reveal that analog switching behavior originates from the controlled formation of multiple nano-filaments within the confined geometry. ANNs implemented with these nano-filaments confined to h-BN memristors exhibit highly linear and symmetric synaptic weight updates, enabling precise training with minimal accuracy degradation. This work establishes multiple nano-filament confinement as a universal design strategy for achieving reliable and linear analog switching in memristors, paving the way for advanced neuromorphic computing.