While considering the broad development prospects of intelligent investment advisers in the future, we must also be aware of the legal supervision issues that intelligent investment advisers bring with them. Simultaneously, big data, artificial intelligence, blockchain, and other technologies have advanced at a breakneck pace during this time. Many new technologies have been used in the financial sector. In the financial field, there is a trend toward gradual integration of finance and technology. The transformation of finance from concept to actual service has been realized thanks to emerging technologies. This combination of finance and technology is known as “financial technology.” The legal supervision mechanism of a recommendation algorithm based on intelligent data recognition is investigated in this paper. On the one hand, it raises the bar for legal knowledge required by the legal supervision mechanism of recommendation algorithm, but on the other hand, it may reduce service efficiency and quality. As a result, using the understanding method of users’ consulting intention while taking into account users’ legal knowledge level, social attributes, and emotional status, it is a feasible and effective way to provide personalized and diverse legal consulting services to the general public.
There is a lack of fairness in market competition and disorder of order as a result of the lack of supervision. OCH (online car-hailing) driver accidents are also reported frequently, causing a slew of social and traffic safety issues. In this paper, we reconstruct the logical path of OCH platform data legal supervision in China’s big data era. We propose a big data encryption algorithm based on data redundancy technology that combines the characteristics of the ECC (Elliptic Curve Cryptography) and AES (Advanced Encryption Standard) block cipher modes in terms of computing speed, parallelism, and security. The system can process large amounts of network data and detect distributed denial of service (DDOS) attacks in real time. The observed feature change trend charts before and after the attacks show significant differences, demonstrating that the proposed features can better distinguish normal traffic from abnormal traffic.
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