As the field of artificial intelligence continues to evolve, it generates an escalating need for intensive computational resources and novel computing architectures. As a new generation of non-volatile memory, memristors can simulate biological synapses. This makes them ideal for neuromorphic computing, enabling brain-like learning and reasoning to significantly enhance computational capabilities. Current research on memristor dielectric materials primarily focuses on transition of metal oxides, perovskites, and organic polymers. Among these, the transition metal oxide TiO₂ is widely used for the switching layer due to its high dielectric constant and excellent thermal stability. However, TiO₂-based memristors face challenges including poor stability and inadequate analog performance, which limit their application in neuromorphic computing. This study developed a high-performance analog memristor using an aMoS₂/a-TiO₂ (amorphous MoS₂/ amorphous TiO₂) heterostructure, achieving over 200 stable cycles and a long data retention time exceeding 10⁴ seconds. This device demonstrates a lower threshold voltage, higher endurance, and superior data retention, as compared to previously reported TiO₂-based heterostructure memristors. Furthermore, various voltage sweep schemes were designed to successfully implement multi-level conductance modulation in the W/a-MoS₂/a-TiO₂/Pt device. The resistive switching mechanism of the W/a-MoS₂/a-TiO₂/Pt device was elucidated by combining conductive mechanism fitting with a physical model that attributes the switching to the localized formation and rupture of conductive filaments. Finally, synaptic functions like LTP and LTD were implemented in the device using square-wave pulses. A convolutional neural network leveraging these functions achieved a 95.8% accuracy in handwritten digit recognition. This study developed a W/a-MoS₂/a-TiO₂/Pt heterostructure that significantly enhances analog memristive performance, providing an effective strategy for improving transition metal oxide-based memristors.