Objective: Stereoelectroencephalogram (SEEG) has been widely adapted to detect the electrical activity of patients with epilepsy. Due to the low-quality, large-amount, high-dimensionality characteristics of SEEG data, it is still challenging to comprehensively employ the SEEG signals to automatically and precisely determine the seizure onset zone (SOZ). This is because there is lack of an effective criterion for clinicians to select the target electrodes, which is of great importance for SOZ localization. Methods: We propose a SOZ localization method via analyzing the long-term SEEG monitoring for preoperative planning of epilepsy surgery. Considering that high frequency oscillations can reflect physiological brain activity of epileptic patients, we first extract the high-frequency features of the SEEG signals and utilize the convolutional neural network (CNN) to detect the interictal and seizure segments. Then we propose a novel criterion, namely adaptive high frequency epileptogenicity index (AHFEI), to determine the target electrodes. Results: We compare our SEEG-determined target electrodes with three preoperative planning of successful focal epilepsy resective surgery cases, finding that most localization results of our method are in consistent with clinical successful decision making, while the performance of our method outperforms than the state-of-the-art method for SOZ localization. Conclusion: Our SEEG-determined SOZ localization method can assist clinicians in preliminarily selecting the potential target electrodes according to long-term SEEG data automatically and effectively. Clinical Impact: The proposed automatic SOZ localization method has achieved satisfactory performance in the preliminary study, which has the great potential to be integrated into function-structure fused clinical decision-making system.