In the domain of continuous control, deep reinforcement learning (DRL) demonstrates promising results. However, the dependence of DRL on deep neural networks (DNNs) results in the demand for extensive data and increased computational complexity. To address this issue, a novel hybrid architecture for actor-critic reinforcement learning (RL) algorithms is introduced. The proposed architecture integrates the broad learning system (BLS) with DNN, aiming to merge the strengths of both distinct architectural paradigms. Specifically, the critic network is implemented using BLS, while the actor network is constructed with a DNN. For the estimations of the critic network parameters, ridge regression is employed, and the parameters of the actor network are optimized through gradient descent. The effectiveness of the proposed algorithm is evaluated by applying it to two classic continuous control tasks, and its performance is compared with the widely recognized deep deterministic policy gradient (DDPG) algorithm. Numerical results show that the proposed algorithm is superior to the DDPG algorithm in terms of computational efficiency, along with an accelerated learning trajectory. Application of the proposed algorithm in other actor-critic RL algorithms is suggested for investigation in future studies.