Driver's driving style has a great impact on lane changing behavior, especially in scenarios such as freeway on-ramps that contain a strong willingness to change lanes, both in terms of inter-vehicle interactions during lane changing and in terms of the driving styles of the two vehicles. This paper proposes a study on game-theoretic decision-making for lane-changing on highway on-ramps considering driving styles, aiming to facilitate safer and more efficient merging while adequately accounting for driving styles. Firstly, the six features proposed by the EXID dataset of lane-changing vehicles were subjected to Principal Component Analysis (PCA) and the three principal components after dimensionality reduction were extracted, and then clustered according to the principal components by the K-means algorithm. The parameters of lane-changing game payoffs are computed based on the clustering centers under several styles. Secondly, a neural network model is designed based on the Matlab nprtool and the principal components taken out earlier as well as the resultant data of clustering are used as inputs to train the model and realize driving style recognition. Next, the freeway ramp lane-changing game is designed, and according to the lane-changing characteristics, the designed lane-changing gains include: speed gain, safety gain, and forced lane-changing gain. The driving style lane change game gain parameters previously derived are matched to the corresponding lane change gains, then the game is solved by Nash equilibrium to get the final lane change results. Finally, the proposed freeway on-ramp lane-changing game decision-making method for freeway on-ramp considering driving styles is validated by simulation under different locations and speeds of freeway on-ramp.