The research employs EEG in combination with the 1DCNN-LSTM algorithm for the purposes of identifying and classifying people with varying risks of schizophrenia. This collection of data for schizophrenia's high-risk and low-risk groups comprises EEG signals from 98 people, the division was made between a group of 50 with normal EEG signals and another group of 48 with high-risk indicators for schizophrenia. The duration of measurement for every participant stood at 12 minutes. The data set was processed in advance, encompassing tasks like channel exclusion, re-referencing, dividing the dataset, performing independent component analysis (ICA), windowing, normalizing, and segregating it into the testing, training, and validation sets. Subsequently, the processed EEG data was integrated into the 1DCNN-LSTM classification model, where post-extensive learning, the model's weights were derived. The categorization system attained a 94.26% precision rate in the identification of the complete-channel EEG data collection. This research utilized the 1DCNN-LSTM algorithm to classify and identify high-risk and low-risk groups for schizophrenia, showcasing an adequate recognition capability that satisfies real-world application criteria. The system precisely categorizes populations at varying risk levels for schizophrenia through comprehensive EEG data, thus facilitating precise schizophrenia detection and offering prompt diagnosis and treatment for those at high risk for the condition.