The invention provides a method for CSI-based fine-grained gesture recognition, wherein the method comprises the following steps: determining a start point, an end point, a velocity, a direction and/or an inflection point of at least one stroke gesture in multiple dimensions according to an eigenvalue of channel state information; dividing the strokes according to the start point, the end point, the velocity, the direction and/or the inflection point of the stroke using a machine learning method and forming a stroke sequence; building a stroke decipherment model according to frequencies of the strokes appearing in natural language rules and/or scientific language rules and/or connection rules between the strokes; and dividing and recognizing the stroke sequence as a letter sequence, a radical sequence, a numeral sequence and/or a pattern sequence conforming to the natural language rules and/or the scientific language rules using the stroke decipherment model. The present invention involves recognizing strokes of characters from finger gesture, and then recovering characters from the strokes, so as to enrich types of languages that can be recognized from finger gesture and enhance recognition accuracy of gesture writing.