In distributed optimization, the practical problem-solving performance is essentially sensitive to algorithm selection, parameter setting, problem type, and data pattern. Thus, it is often laborious to acquire a highly efficient method for a given specific problem. In this article, we propose a learning-based method to achieve efficient distributed optimization over networked systems. Specifically, a deep reinforcement learning (DRL) framework is developed for adaptive configuration within a parameterized unifying paradigm, which incorporates an abundance of decentralized first-order and second-order optimization algorithms. We exploit the local consensus and objective information to represent the regularities of problem instances and trace the solving progress, which constitute the states observed by a DRL agent. The framework is trained on a number of practical problem instances of similar structures yet different problem data. Experiments on various problems demonstrate that our proposed method outperforms several state-of-the-art distributed optimization algorithms in terms of convergence speed and solution accuracy.