To address the challenging issues in task offloading and resource allocation within the Internet of Vehicles (IoV) environments, such as multi-objective task offloading, hybrid action space decision-making, and low training efficiency, a hybrid asynchronous advantage actor-critic (H-A3C) reinforcement learning algorithm was proposed by integrating the hybrid action representation (HyAR) framework with an improved asynchronous training mechanism. The optimization problem of multi-objective task offloading and resource allocation in IoV was investigated. In this approach, a parameterized action Markov decision process (PAMDP) was constructed to uniformly model the coupling relationship between discrete offloading target selection and continuous resource allocation. A conditional variational autoencoder (CVAE) was designed to achieve a latent space representation of hybrid actions, and a time-decaying gradient aggregation mechanism was introduced to enhance the stability of asynchronous training. The results demonstrate that H-A3C significantly outperforms relevant baseline algorithms in terms of convergence speed and stability. The H-A3C algorithm stabilizes after approximately 3 500 training episodes, which is about 40% faster than the standard A3C algorithm. In terms of cumulative system reward, its final return is improved by 25% compared to the PADDPG algorithm and by 35% compared to the standard A3C. Under high vehicle density conditions (more than 110 vehicles), by precisely adapting the offloading ratio, H-A3C increases edge resource utilization by about 18% and reduces reliance on cloud resources by 30%, leading to superior system returns. In computationally intensive task scenarios, its task success rate is 15% and 22% higher than that of PADDPG and standard A3C, respectively. Moreover, it can dynamically adjust optimization weights, reducing the total processing latency by approximately 20% and leasing costs by about 15%. thereby achieving a better latency-cost trade-off and effectively lowering the overall processing time and rental expenses.