The execution capacity of service-oriented system relies on the third-party services. However, such reliance would result in uncertainties in consideration of the complex and changeable network environment. Hence, runtime monitoring technique is required for service-oriented system. Effective monitoring technique towards Web QoS, which is an important measure of third-party service quality, is necessary to ensure quality control on Web service. Several monitoring approaches have been proposed, however none of them consider the influences of environment including the position of server and user usage, and the load at runtime. Ignoring these influences, which exist among the real-time monitoring process, may cause monitoring approaches to produce wrong results. To solve this problem, this paper proposes a new environment sensitive Web QoS monitoring approach, called wBSRM (weighted Bayes runtime monitoring), based on weighted naive Bayes and TF-IDF (Term Frequency-Inverse Document Frequency). The proposed approach is inspired by machine learning classification algorithm, and measures influence of environment factor by TF-IDF algorithm. It constructs weighted naïve Bayes classifier by learning part of samples to classify monitoring results. The results that meet QoS standard are classified as c₀, and those that do not meet is classified as c₁. Classifier can output ratio between posterior probability of c₀ and c₁, and the analysis can lead to three monitoring results including c₀, c₁ or inconclusive. Experiments are conducted based on both public network data set and randomly generated data set. The results demonstrate that this approach is better than previous approaches by accurately calculating environment factor weight with TF-IDF algorithm and weighted naïve Bayes classifier.