The invention relates to the technical field of streaming data anomaly detection and more specifically relates to an aluminum profile extrusion process stream data anomaly detection method and devicebased on an isolated forest algorithm and a storage medium. The method comprises the following steps: S10, reading original stream data of an extrusion process of an extruder, and initializing a multi-feature semi-space isolated forest model through the original stream data; S20, entering a detection period, and using the multi-feature semi-space isolation forest model to perform anomaly detectionon the stream data in the current period; S30, judging whether the detection period is finished or not; if not, returning to the step S20, updating the detection period, and if so, entering the nextstep; S40, judging whether the abnormal rate of the current period is greater than a threshold value or not, if so, indicating that concept drift exists, updating the model by using the data of the current period; if not, returning to the step S20, and entering the next period for detection until all periods are detected. The model can be updated in real time, and the problem that abnormal detection results are inaccurate due to noise and concept drift existing in streaming data is solved.