Named-Data Networking (NDN) is a new communication paradigm where network primitives are based on named-data rather than host identifiers. Compared with IP, NDN has a unique feature that forwarding plane enables each router to select the next forwarding hop independently without relying on routing. Therefore, forwarding strategies play a significant role for adaptive and efficient data transmission in NDN. Most of the existing forwarding strategies use fixed control rules based on simplified or inaccurate models of the deployment environment. As a result, existing schemes inevitably fail to achieve optimal performance across a broad set of network conditions and application demands. In this paper, We propose IFS-RL, an intelligent forwarding strategy based on reinforcement learning. IFS-RL trains a neural network model which chooses appropriate interfaces for the forwarding of Interest based on observations collected by routing node. Not relying on pre-programmed models, IFS-RL learns to make decisions solely through observations of the resulting performance of past decisions. Therefore, IFS-RL can implement intelligent forwrarding which adapt to a wide range of network conditions. Besides, we also researches the learning granularity and the enhancement for network topology change. We compare IFS-RL to state-of-the-art forwarding strategies in ndnSIM. Experimental results show that IFS-RL can achieve higher throughput and lower packet drop rates.