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A model is utilized in this paper to analyze the textual emotions of ideology in Marxist theory. Long and short-term memory networks are chosen as the main method of text analysis to construct the main process of Marxist ideology education. Combined with the hybrid self-attention mechanism, the efficiency of extracting data features from the text was improved. The results show that the ALBERT-SABL-based sentiment analysis model is 86.9% accurate in extracting the sentiment of the ideology, and the F1 value is 87.6%. Compared with TextCnn, the accuracy has improved by 1.8%. Different schools have different levels of identification with Marx’s ideology, and under the identification dimension, the highest sentiment identification dimension among the eight school samples is School 2, with a dimension of 100. This study provides reference data in the ideological education of Marxist theory and promotes the development of Marx’s ideology.