Continuous and accurate respiratory volume monitoring during sleep helps to infer the user’s sleep stage and provide clues about some chronic diseases. The existing works mainly focus on the detection and monitoring of respiratory frequency, and lack the means for continuous monitoring of respiratory volume. Therefore, a system named RF-SLEEP which uses commercial Radio Frequency IDentification (RFID) tags to wirelessly sense the respiratory volume during sleep was proposed. The phase value and timestamp data returned by the tag array attached to the chest surface was collected continuously by RF-SLEEP through the reader, and the displacement amounts of different points of the chest caused by breathing were calculated, then the model of relationship between the displacement amounts of different points of the chest and the respiratory volume was constructed by General Regression Neural Network (GRNN), so as to evaluate the respiratory volume of user during sleep. The errors in the calculation of chest displacement caused by the rollover of the user’s body during sleep were eliminated by RF-SLEEP through attaching the double reference tags to the user’s shoulders. The experimental results show that the average accuracy of RF-SLEEP for continuous monitoring of respiratory volume during sleep is 92.49% on average for different users.