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A Study of the Effectiveness of Using Deep Learning Algorithms to Analyze Legal Risk Identification in Social Work Programs

Xinxin Fan

Xinxin Fan

Humanities College, Puyang Vocational and Technical CollegePuyang, China
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Fan, Xinxin
  
24 mars 2025
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Publié en ligne: 24 mars 2025
Reçu: 15 nov. 2024
Accepté: 18 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0769
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© 2025 Xinxin Fan, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Organization framework of social work agencies
Organization framework of social work agencies

Figure 2.

Deep seeding network
Deep seeding network

Figure 3.

Change in the loss curve
Change in the loss curve

Figure 4.

Change in the accuracy curve
Change in the accuracy curve

Figure 5.

Experimental comparison
Experimental comparison

The identification of the risk elements of the law

Model Legal risk
S1 S2 S3 S4 S5 S6 Mean
HMM 93.85 80.85 48.15 32.42 54.58 83.18 65.505
CRF 95.73 93.12 48.25 35.15 58.92 94.32 70.915
BiLSTM 96.15 96.11 50.12 41.53 91.04 94.38 78.222
BiLSTM-CRF 97.63 94.60 48.15 55.35 91.53 95.21 80.412
LSTM 96.99 86.15 50.02 40.15 92.65 94.21 76.695
BERT-CRF 97.42 95.35 51.36 57.82 93.32 96.15 81.903
Ours 97.52 95.32 71.35 70.15 96.68 97.89 88.152
Langue:
Anglais
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1 fois par an
Sujets de la revue:
Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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