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Instrumentation is an indispensable part of the application performance management (APM) and used for tracking and obtaining the information of software system. Nowadays, there are various instrumentation tools, and the probes of the tools are unchanged during target application operation. However, in daily exception and bug detection, users require different code monitoring in different periods of a target application operation, and too many probes may have the result that the instrumentation tool wastes and consumes more system resources; too few probes may result that APM can’t locate accurate positioning failure of target software. We proposes a new adaptation instrumentation framework based on analyzing performance data and carries out adaption instrumentation based on liner regression and K-Means, changes the probes dynamically during operation and reduces the resource waste as much as possible. What’s more, this paper uses Na?ve Bayes to filter Java classes for reducing instrumentation classes and overhead; In comparison to the traditional tools, this paper leads to low overhead and disturbance through the experiment, average page response time reduced by about 6.88%.