Immersive training camp and multi-dimensional portrait-based school recruitment talent evaluation method

Abstract

The invention relates to the technical field of talent evaluation, in particular to a school recruitment talent evaluation system and method based on an immersive training camp and a multi-dimensional portrait, and the system comprises a precise interview preliminary screening module which is used for carrying out the preliminary screening of candidates through a behavior interview method, and an immersive training camp management module which is configured to simulate a real working scene, the method comprises the following steps: organizing candidates to participate in a training task for several days, a multi-dimensional behavior data acquisition module for recording behavior indexes of the candidates in training in real time, including speaking duration, collaborative proposal times and emotional stability data, a talent portrait generation module, analyzing the behavior data based on an algorithm model, and a decision support module, according to the school recruitment talent evaluation system and method based on the immersive training camp and the multi-dimensional portrait, a two-stage evaluation mechanism is adopted, candidates are rapidly screened through a behavior interview method, and then the comprehensive ability of the candidates is observed by simulating the training camp depth of a real working scene.

Description

Technical Field

[0001] This invention relates to the field of talent assessment technology, specifically to a campus recruitment talent assessment system and method based on immersive training camps and multi-dimensional profiles. Background Technology

[0002] The background technology of this invention patent addresses a long-standing systemic deficiency in the field of campus recruitment. Traditional campus recruitment primarily relies on resume screening and a single interview for talent assessment, a method that fails to comprehensively reflect a candidate's actual abilities and potential. Resume information is often limited to academic background and internship experience, failing to effectively assess the application level of professional skills in real-world work scenarios. Interviews, constrained by time and format, often only assess candidates' on-the-spot expression, making it difficult to deeply observe key qualities such as teamwork and resilience.

[0003] Enterprises face information asymmetry in their decision-making, leading to higher recruitment risks. Screening criteria overemphasize explicit qualifications while neglecting value alignment and long-term development potential, resulting in high employee turnover rates after onboarding. On the candidate side, the one-way evaluation process lacks engagement and feedback mechanisms, negatively impacting employer brand image. University-enterprise cooperation remains at a superficial level, limited to recruitment presentations, making it difficult for universities to obtain effective feedback on corporate hiring standards, resulting in a disconnect between talent development and market demands.

[0004] In existing technologies, some companies have attempted to introduce online assessments or group discussions, but the assessment content has low relevance to actual work tasks, and the data collection dimensions are limited. Group discussions and similar formats still rely heavily on subjective observation and lack support from quantitative behavioral indicators. These methods cannot systematically solve fundamental problems such as distorted assessment scenarios, weak data support, and insufficient potential exploration. There is an urgent need to build an assessment system that integrates real-world scenario simulation with multi-dimensional data analysis to improve the scientific rigor and effectiveness of campus recruitment.

[0005] To this end, we propose a campus recruitment talent evaluation system and methodology based on immersive training camps and multi-dimensional profiles. Summary of the Invention

[0006] One of the technical problems this application aims to solve is the urgent need to build an evaluation system that integrates real-world scenario simulation with multi-dimensional data analysis in order to improve the scientific rigor and effectiveness of campus recruitment.

[0007] To address the aforementioned technical issues, this application provides a campus recruitment talent assessment system based on immersive training camps and multi-dimensional profiles, including a precise interview screening module for preliminary screening of candidates using behavioral interviewing methods. The immersive training camp management module is configured to simulate real work scenarios and organize candidates to participate in intensive training tasks lasting several days. The multi-dimensional behavioral data collection module records candidates' behavioral indicators in the training camp in real time, including speaking time, number of collaborative suggestions, and emotional stability data. The talent profile generation module analyzes behavioral data based on algorithm models and outputs a comprehensive potential score that includes professional skills, teamwork, leadership, stress resistance, and cultural fit. The decision support module generates hiring recommendations based on the comprehensive potential score.

[0008] In some embodiments, the multi-dimensional behavioral data acquisition module integrates sensors and video analysis units to quantify the dynamic interactive behavior of candidates in team tasks.

[0009] In some embodiments, the talent profile generation module uses machine learning algorithms to map behavioral indicators to preset evaluation dimensions to generate a quantifiable three-dimensional talent profile.

[0010] In some embodiments, the immersive training camp management module includes a scenario customization unit, which supports configuring high-pressure challenges and cross-departmental collaborative simulation tasks according to the needs of enterprise positions.

[0011] In some embodiments, the decision support module links to a university-enterprise cooperation database and provides feedback on talent evaluation results to universities to optimize subsequent training programs.

[0012] In some embodiments, the campus recruitment talent evaluation method based on immersive training camps and multi-dimensional profiles includes the following steps: S1: Conduct an initial screening of candidates using behavioral interviewing to select those who will enter the training camp; S2: Organize candidates to participate in an immersive training camp, where they perform team tasks in simulated work scenarios; S3: Real-time collection of candidate behavior data, including frequency of proactive speaking, conflict resolution behavior, and task response timeliness; S4: Analyze behavioral data based on algorithmic models to generate a comprehensive score covering potential dimensions and suitability; S5: Generate talent recruitment decisions based on comprehensive scores and preset thresholds.

[0013] In some embodiments, behavioral data collection includes quantifying and recording indicators of a candidate’s emotional fluctuations during high-pressure tasks and marking key decision-making nodes with timestamps.

[0014] In some embodiments, the algorithm model employs a weighted aggregation algorithm to integrate various behavioral indicators into a comprehensive potential score based on the job requirements weight.

[0015] In some embodiments, the immersive training camp's task design includes a cultural fit test, observing candidates' behavioral choices through value conflict scenarios.

[0016] In some embodiments, after generating a hiring decision, a multi-dimensional talent profile report is provided to the candidate, and data analyzing talent capability shortcomings is output to partner universities.

[0017] This invention has at least the following beneficial effects: 1. A two-stage evaluation mechanism is adopted. First, candidates are quickly screened through behavioral interviews. Then, a training camp simulating real work scenarios is used to deeply observe the candidates' comprehensive abilities. During the training camp, the system collects behavioral data such as speaking time, number of collaborative suggestions, and emotional stability in real time. Combined with the algorithm model, a three-dimensional talent profile covering professional ability, collaboration ability, leadership, stress resistance and cultural fit is generated to provide quantitative basis for recruitment decisions.

[0018] 2. Through immersive scenario design, candidates can naturally demonstrate their behavioral traits in complex environments such as simulated high-pressure tasks and cross-departmental collaborations, enabling companies to capture potential abilities that cannot be reflected in resumes. Meanwhile, algorithm-driven data analysis reduces subjective judgment bias and improves the accuracy of talent matching. Data shows that this model not only increases the admission rate of students from 211 and first-tier universities, but also significantly improves the signing rate of specialized training programs.

[0019] 3. The decision-making module can provide feedback to universities on talent assessment results and competency gap analysis, promoting the alignment of talent development with corporate needs. For candidates, the feedback from the multi-dimensional profile report enhances engagement and transparency, optimizing the application experience. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the system composition of the present invention; Figure 2 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1, see Figure 1 The present invention provides a technical solution: a campus recruitment talent evaluation system based on immersive training camp and multi-dimensional profile, including: a precise interview screening module, used to conduct preliminary screening of candidates through behavioral interviewing methods; The immersive training camp management module is configured to simulate real work scenarios, organizing candidates to participate in intensive training tasks lasting several days; the multi-dimensional behavioral data collection module records candidates' behavioral indicators in the training camp in real time, including speaking time, number of collaborative suggestions, and emotional stability data; the talent profile generation module analyzes behavioral data based on algorithm models and outputs a comprehensive potential score that includes professional ability, teamwork, leadership, stress resistance, and cultural fit; and the decision support module generates recruitment recommendations based on the comprehensive potential score.

[0023] The multi-dimensional behavioral data acquisition module integrates sensors and video analysis units to quantify candidates' dynamic interactive behaviors in team tasks.

[0024] The talent profile generation module uses machine learning algorithms to map behavioral indicators to preset evaluation dimensions, generating a quantifiable, three-dimensional talent profile.

[0025] The immersive training camp management module includes a scenario customization unit, which supports configuring high-pressure challenges and cross-departmental collaborative simulation tasks according to the needs of enterprise positions.

[0026] The decision support module connects to the university-enterprise cooperation database and provides feedback on talent evaluation results to universities to optimize subsequent training programs.

[0027] The quantitative data on leadership was collected from group tasks in the training camp. Specific behavioral data included: initiative frequency (the number of times a substantive solution was first proposed in a discussion); resource coordination behavior (the number of times a role or task was proactively assigned to team members); consensus-building behavior (the number of times a compromise solution was proposed and adopted by the team in the event of disagreement); and the proportion of speaking time (the percentage of time spent speaking in project reports or discussions, obtained through voice analysis technology).

[0028] The quantitative data on resilience was collected from task scenarios incorporating "sudden challenges." Specific behavioral data included: Task switching efficiency refers to the time (in seconds) required to re-engage with work after receiving a sudden new task. Emotional stability index is the assessment of emotional fluctuation values ​​before and after stressful tasks through facial expression analysis (with consent) or verbal emotion analysis; solution-oriented statement ratio is the ratio of statements such as "Let's try method X next" versus "This is too difficult" when facing setbacks.

[0029] The mathematical model and calculation process of the talent profile generation module are as follows: The first step is to standardize the behavioral indicators: Let the original behavioral data of the i-th candidate be a vector. , where n is the number of data points collected (e.g., speaking duration, heart rate coefficient of variation, etc.). This represents the original value of the k-th behavioral indicator (such as speaking duration, heart rate variability coefficient, etc.). The influence of dimensions is eliminated through range standardization. Generate standardized vectors in is the standardized value of the kth index, ranging from [0,1]. The minimum value among all candidates for the k-th indicator. It is the maximum value among all candidates for the k-th indicator.

[0030] Then comes the feature dimension mapping: Define the set of evaluation dimensions Specifically, professional competence, teamwork, leadership, resilience, and cultural fit are assessed, and an indicator-dimensional mapping matrix is ​​established. : ,in This indicates that the k-th behavioral indicator relates to the dimension. The contribution weights are calculated by training a random forest model with historical data, calculating feature importance, and then normalizing it. For example, the percentage of speaking time (…). In terms of collaborative ability ( The weight of ) It is 0.3, while in terms of compressive strength ( The weight of ) It is 0.05.

[0031] Next is the calculation of dimensional scores: The dimensional scores are generated using a weighted aggregation algorithm: , thus obtaining the dimension score vector ,in For the i-th candidate in dimension The score, The value of the k-th index after standardization. Let k be the weight of dimension j. This represents the candidate's score vector across five dimensions.

[0032] Next is the adjustment of job suitability: Define dimension weight vectors based on the requirements of the target position. Calculate the overall score ,in , The final score for the candidate's job fit ranges from [0, 100]. For example, the weight for technical positions. This indicates that professional skills account for the highest proportion.

[0033] Finally, the image visualization output is provided: Will Convert parameters to polar coordinates to generate a radar chart: Radius axis normalization: ,in Representing dimensions Radius length on the radar chart (normalized to a percentage); This represents the minimum / maximum score across all candidate dimensions. Angular axis uniformly distributed: This forms a five-dimensional closed graph, visually demonstrating the capability structure, in which... Representing dimensions Angles in polar coordinates (five-dimensional equidistant distribution).

[0034] Algorithm implementation process: Random forest model training (used to determine the mapping matrix) ): Input: Historical candidate behavior dataset Dimensional scores annotated by experts (Supervised learning tags); Generate a set of decision trees: each tree is randomly selected. A subset of features; Calculate feature importance: ,in This represents the total number of decision trees in the random forest. This represents the initial variance of the dimension scores. Indicated by indicator The variance after segmentation represents the index. The contribution of [the system] to reducing prediction errors.

[0035] Normalized importance is the mapping weight: The feature importance is converted into a weight allocation for each dimension.

[0036] For example: Standardized behavioral data vectors: Let the candidate's standardized behavioral data vector be: , Corresponding to six behavioral indicators: Indicates the frequency of active speaking (normalized value). Indicates the efficiency of conflict resolution (standardized value). Indicates the task response time (standardized value). This indicates the stability of high-pressure emotions (standardized value). This represents the resource coordination capability (standardized value). This indicates a tendency to compromise on values ​​(standardized value).

[0037] Weighting of Finance Position Dimension: Weighting of resilience dimension: Weight vector of indicators for resilience dimension (j=4): Correspondence: Indicates the weight of the frequency of active speaking. Indicates the conflict resolution efficiency weight. Indicates the weight of task response time. Indicates the weight of high-pressure emotional stability. Indicates the weight of resource coordination capability. This indicates a tendency to compromise on values.

[0038] Calculation of compressive strength score: ; The final overall scores for professional competence (78.3), teamwork (85.1), leadership (76.8), resilience (81.15), and cultural fit (80.2) are as follows: Specifically, the design principles of this campus recruitment talent assessment system stem from an analysis of the shortcomings of traditional recruitment models. Traditional methods rely excessively on resumes and single interviews, making it difficult to capture a candidate's comprehensive performance in real-world work scenarios, leading to a disconnect between talent assessment and actual needs. The system addresses this fundamental contradiction through a two-stage architecture: the initial screening stage uses behavioral interviewing to quickly filter out those who do not meet basic standards, focusing on key candidates for the core assessment phase.

[0039] The purpose of immersive training camps is to force candidates to naturally reveal their behavioral patterns under near-real-world work pressure and team interaction by simulating high-pressure tasks and cross-departmental collaboration. The intervention of sensor and video analytics technologies makes implicit abilities in teamwork explicit. For example, by dynamically capturing speaking time and suggestion frequency, communication initiative can be quantified, while emotional stability data reflects psychological qualities under high pressure. These are dimensions that traditional interviews cannot observe.

[0040] The algorithm design of the talent profile generation module is for the integration and transformation of multi-dimensional indicators. The machine learning model maps fragmented behavioral data to preset dimensions such as professional ability and teamwork, avoiding the one-sidedness of a single indicator. The comprehensive potential score essentially establishes a mathematical relationship between behavioral characteristics and job competence, making subjective qualities have comparable quantitative standards and providing an objective basis for corporate decision-making.

[0041] The design of the decision-making module, which links to the university-enterprise database, aims to go beyond simply pushing recruitment suggestions. It establishes a closed-loop mechanism by providing feedback on evaluation results to universities. Companies then feed back data on talent shortages to the education sector, prompting universities to adjust their training programs and narrowing the gap between talent supply and corporate demand from the outset. For candidates, immersive participation itself provides a deep experience of the company culture, and the two-way selection process enhances the accuracy of decision-making.

[0042] The benefits of this system are that it breaks away from the traditional linear screening logic that starts with resumes, and constructs a three-dimensional model of scenario immersion, behavior quantification, profile generation, and ecosystem feedback. Companies reduce the risk of wrong hiring due to information gaps, and the increased signing rate of specialized training programs confirms the optimized matching accuracy. Universities gain dynamic guidance on the talent market, while candidates build identification with employer brands through deep participation. This multi-party synergy drives campus recruitment to evolve from transactional selection to ecosystem-based cultivation.

[0043] Example 2, see Figure 2 The campus recruitment talent evaluation method based on immersive training camps and multi-dimensional profiles includes the following steps: S1: Conduct an initial screening of candidates using behavioral interviewing to select those who will enter the training camp; S2: Organize candidates to participate in an immersive training camp, where they perform team tasks in simulated work scenarios; S3: Real-time collection of candidate behavior data, including frequency of proactive speaking, conflict resolution behavior, and task response timeliness; S4: Analyze behavioral data based on algorithmic models to generate a comprehensive score covering potential dimensions and suitability; S5: Generate talent recruitment decisions based on comprehensive scores and preset thresholds.

[0044] Behavioral data collection includes quantifying and recording indicators of candidates' emotional fluctuations during high-pressure tasks and marking key decision-making nodes with timestamps.

[0045] The algorithm model uses a weighted aggregation algorithm to integrate various behavioral indicators into a comprehensive potential score based on the job requirements weight.

[0046] The immersive training camp's task design includes a cultural adaptation test, observing candidates' behavioral choices through value conflict scenarios.

[0047] After generating a hiring decision, a multi-dimensional talent profile report is provided to the candidate, and data analyzing talent capability shortcomings is output to partner universities.

[0048] Specifically, traditional processes rely on resume screening and a single interview, making it difficult to comprehensively assess a candidate's performance in a real work environment, leading to a disconnect between talent assessment and actual needs. This method employs a two-stage assessment framework: first, behavioral interviews quickly identify candidates who meet basic competencies; then, an immersive training camp is used to deeply observe potential abilities, forming a progressive screening logic.

[0049] The purpose of immersive training camps is to force candidates to naturally reveal their behavioral patterns in a near-real-world work environment by simulating complex scenarios such as high-pressure tasks and cross-departmental collaboration. Embedding value conflict scenarios within the tasks allows for the observation of candidates' implicit traits regarding cultural fit. This design breaks through the performative limitations of traditional interviews, shifting the assessment focus from surface-level responses to genuine behavioral reactions.

[0050] By quantifying indicators such as speaking frequency, conflict resolution methods, and task response timeliness, abstract qualities like communication skills and adaptability are transformed into analyzable data. The capture of emotional fluctuation indicators, combined with timestamps, allows for the tracing of candidates' decision-making logic and psychological resilience under high pressure, compensating for the neglect of implicit traits in traditional assessments.

[0051] The algorithm model design emphasizes multi-dimensional integration. The weighted aggregation algorithm does not simply add up data, but dynamically adjusts the indicator weights based on job requirements, ensuring that the overall score reflects the true differences in job suitability. For example, technical positions may emphasize task response timeliness, while management positions may amplify the weight of leadership behaviors. This dynamic mapping mechanism ensures a precise correspondence between the scoring results and the hiring needs.

[0052] Providing candidates with multi-dimensional profile reports not only enhances the transparency of the selection process but also helps them identify their weaknesses. Providing talent capability analyses to universities creates a closed loop of university-industry collaboration. Universities can adjust their training programs based on enterprise feedback, bridging the gap between talent supply and market demand from the outset, while enterprises gain a more suitable pool of future talent.

[0053] The advantages of this method are that it achieves authenticity in capability observation through immersive scenarios, ensures objectivity in assessment through data quantification, and improves matching accuracy through algorithm fusion, ultimately forming a virtuous cycle of "deep assessment - precise matching - ecosystem optimization." Enterprises reduce the risk of wrong hiring, universities optimize their training programs, and candidates receive growth feedback. This win-win mechanism promotes the transformation of campus recruitment from one-way selection to ecosystem co-construction.

[0054] The table below shows the number of students recruited by a company in different years through campus recruitment using this system and method.

[0055] As shown in the table above, the percentage of students admitted from 211 universities (especially for master's programs) has increased compared to previous years; the percentage of students admitted from first-tier universities has increased (14%); and the percentage of students admitted from second-tier universities has decreased significantly (-17%). The table below shows the number of students admitted from different universities by a company using this system and method in its campus recruitment efforts.

[0056] Data shows that students admitted through specialized training programs had a significantly higher signing rate (13%) compared to those admitted solely through interviews. The leading signing rate among graduates from 211 universities demonstrates the company's appeal to highly educated individuals, highlighting the advantages of this system and methodology in campus recruitment compared to traditional methods.

[0057] Example 3 uses a typical task as an example to illustrate the immersive training camp task scenario and its mapping relationship with the actual job competency.

[0058] A typical task scenario is titled "Cross-Industry Resource Competition - 48 Hours Before New Product Launch." The scenario describes candidates being divided into groups of 4-5 people to act as a new product team. During the project, the simulated "marketing department" suddenly requests the addition of a major, unplanned feature; the "technology department" informs them that existing core resources have been halved due to unforeseen circumstances; and the "senior management" demands the project be delivered 12 hours ahead of schedule. This reflects the following substantive skill requirements for the position: Conflict resolution skills are essential for handling resource conflicts between the Marketing and Technology departments. Prioritization and project management skills are crucial for re-planning project roadmaps under resource constraints and time pressure. Innovation skills are vital for proposing alternative solutions to meet the needs of multiple parties. Upward management and communication skills are essential for clearly reporting change logic, risks, and countermeasures to senior management.

[0059] Teamwork and resilience are essential for maintaining team morale and collaborative efficiency under continuous high pressure.

[0060] Example 4: The operation process of this system and method is as follows: The campus recruitment assessment system operates in two progressive phases. The initial screening phase employs behavioral interviewing, where interviewers assess candidates' past behavioral patterns through pre-set scenario questions, such as asking them to describe their experience in resolving team conflicts, quickly filtering out those whose basic qualities don't match the initial screening. Candidates who pass the initial screening then proceed to an immersive training camp, typically lasting three to five days, conducted in a setting that simulates a real corporate environment.

[0061] During the training camp, candidates are divided into groups to perform customized tasks. For example, in tech company recruitment, tasks might include developing simple programs within a time limit, processing defective datasets, and developing optimization solutions for resource constraints. Financial companies might design simulated risk control meetings or emergency customer dispute handling scenarios. Value conflict tests are incorporated into the task process, such as requiring candidates to choose between short-term profits and long-term reputation.

[0062] Multi-dimensional data collection was conducted throughout the training camp. Wearable devices monitored physiological indicators such as heart rate variability in real time, panoramic cameras recorded body language and collaborative interactions, and audio recording devices marked the timing and keywords of speeches. The focus was on collecting behavioral indicators such as emotional fluctuation curves under high-pressure tasks, key decision timestamps, and frequency of proactive collaboration.

[0063] Behavioral data is standardized and then input into the algorithm model. The model dynamically allocates weights based on job requirements; for technical positions, it emphasizes professional competence indicators such as solution optimization efficiency, while for management positions, it amplifies the weight of leadership behaviors. A weighted aggregation algorithm generates a comprehensive potential score from 0 to 100 points and outputs a radar chart containing five core dimensions.

[0064] The decision-making module automatically triggers offer notifications based on scoring thresholds. Those not selected receive personalized profile reports highlighting specific skill gaps, such as "strengthening resilience and decision-making timeliness." Simultaneously, the system sends encrypted group capability analyses to partner universities; for example, it identifies a common weakness among this year's students in their ability to pinpoint problems in production environments, prompting universities to adjust their practical courses accordingly.

[0065] The entire process forms a closed-loop mechanism. Companies observe real-world capabilities through highly realistic scenarios, reducing the risk of hiring the wrong candidate. Candidates gain in-depth experience of work scenarios and receive feedback on their abilities. Universities optimize their training programs based on market data, ultimately achieving ecological synergy in talent assessment, selection, and development.

[0066] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0067] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A campus recruitment talent evaluation system based on immersive training camps and multi-dimensional profiles, characterized in that: include: The Precision Interview Screening Module is used to conduct preliminary screening of candidates using behavioral interviewing methods. The immersive training camp management module is configured to simulate real work scenarios and organize candidates to participate in intensive training tasks lasting several days. The multi-dimensional behavioral data collection module records candidates' behavioral indicators in the training camp in real time, including speaking time, number of collaborative suggestions, and emotional stability data. The talent profile generation module analyzes behavioral data based on algorithm models and outputs a comprehensive potential score that includes professional skills, teamwork, leadership, stress resistance, and cultural fit. The decision support module generates hiring recommendations based on the comprehensive potential score.

2. The campus recruitment talent evaluation system based on immersive training camps and multi-dimensional profiles as described in claim 1, characterized in that: The multi-dimensional behavioral data acquisition module integrates sensors and video analysis units to quantify the dynamic interactive behavior of candidates in team tasks.

3. The campus recruitment talent evaluation system based on immersive training camps and multi-dimensional profiles as described in claim 1, characterized in that: The talent profile generation module uses machine learning algorithms to map behavioral indicators to preset evaluation dimensions, generating a quantifiable three-dimensional talent profile.

4. The campus recruitment talent evaluation system based on immersive training camps and multi-dimensional profiles as described in claim 2, characterized in that: The immersive training camp management module includes a scenario customization unit, which supports configuring high-pressure challenges and cross-departmental collaborative simulation tasks according to the needs of enterprise positions.

5. The campus recruitment talent evaluation system based on immersive training camps and multi-dimensional profiles as described in claim 1, characterized in that: The decision support module is linked to the university-enterprise cooperation database and provides feedback on talent evaluation results to universities to optimize subsequent training programs.

6. The campus recruitment talent evaluation method based on immersive training camps and multi-dimensional profiles according to any one of claims 1-5, characterized in that, Includes the following steps: S1: Conduct an initial screening of candidates using behavioral interviewing to select those who will enter the training camp; S2: Organize candidates to participate in an immersive training camp, where they perform team tasks in simulated work scenarios; S3: Real-time collection of candidate behavior data, including frequency of proactive speaking, conflict resolution behavior, and task response timeliness; S4: Analyze behavioral data based on algorithmic models to generate a comprehensive score covering potential dimensions and suitability; S5: Generate talent recruitment decisions based on comprehensive scores and preset thresholds.

7. The campus recruitment talent evaluation method based on immersive training camps and multi-dimensional profiles as described in claim 6, characterized in that: The behavioral data collection includes quantifying and recording indicators of candidates' emotional fluctuations during high-pressure tasks, and marking key decision-making nodes with timestamps.

8. The campus recruitment talent evaluation method based on immersive training camps and multi-dimensional profiles as described in claim 6, characterized in that: The algorithm model adopts a weighted aggregation algorithm, which integrates various behavioral indicators into a comprehensive potential score according to the job requirements weight.

9. The campus recruitment talent evaluation method based on immersive training camps and multi-dimensional profiles as described in claim 6, characterized in that: The immersive training camp's task design includes a cultural adaptation test, which observes candidates' behavioral choices through value conflict scenarios.

10. The campus recruitment talent evaluation method based on immersive training camps and multi-dimensional profiles as described in claim 6, characterized in that: After generating a hiring decision, a multi-dimensional talent profile report is provided to the candidate, and data analyzing talent capability shortcomings is output to partner universities.

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