Quality of service (QoS) is an important criterion to measure the quality of Web services, and it is an important aspect for users to choose Web services. This paper proposes a dynamic weighting Web service QoS monitoring method based on information gain and sliding window mechanism. IgS-wBSRM initializes the environmental factors' weights with a certain amount of initial training samples. It also employs the theory of information entropy and gain to determine the chaotic state of the samples. IgS-wBSRM reads the sample data flow in sequence, calculates the information gain of each impact factor combination after the arrival of sample data unit. Then it updates the initialized weights with TF-IDF in a dynamic environment. In this way, IgS-wBSRM can correct the uneven classification problem between classes and the off-line constant problem in traditional monitoring approach wBSRM. Moreover, considering the timeliness of the training sample data, IgS-wBSRM combines sliding window mechanism to update the weights of each impact factor combination, so that it can eliminate the impact on the recent service running state that the accumulated historical data bring. The experiment results under a real world QoS Web service data set demonstrate that with the sliding window mechanism, IgS-wBSRM can abandon the expiration information of historical data effectively, and the dynamic weighting method combined with sliding window mechanism and information gain can monitor the QoS more accurately. The overall monitoring effect is markedly better than existing QoS monitoring approaches.