The invention discloses a text feature extraction method based on machine learning, and the method comprises the following steps: 1, initializing a system; 2, inputting data; 3, carrying out part-of-speech tagging; 4, training a machine learning block model; 5, carrying out text partitioning; and 6, carrying out text output; wherein in the step 1), a display is arranged on the outer wall of one side of the SVO block text extractor, in the step 2), data of external equipment can be input into the SVO block text extractor through an interactive network module, and in the step 5), SVO block texts are semantically related mark groups.According to the method, a plurality of part-of-speech marks are identified in an unstructured text; a plurality of SVO block texts are determined from a plurality of part-of-speech tags by using a machine learning block model, and the machine learning block model is trained on training data marked with a subject-verb-object (SVO), so that the time cost of text feature extraction is reduced, and manual development and rule updating are not needed.